diff --git "a/data/it/train.jsonl" "b/data/it/train.jsonl" new file mode 100644--- /dev/null +++ "b/data/it/train.jsonl" @@ -0,0 +1,1992 @@ +{"source": "Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural networks from a theoretical aspect.Particularly, the properties of critical points and the landscape around them are of importance to determine the convergence performance of optimization algorithms.In this paper, we provide a necessary and sufficient characterization of the analytical forms for the critical points (as well as global minimizers) of the square loss functions for linear neural networks.We show that the analytical forms of the critical points characterize the values of the corresponding loss functions as well as the necessary and sufficient conditions to achieve global minimum.Furthermore, we exploit the analytical forms of the critical points to characterize the landscape properties for the loss functions of linear neural networks and shallow ReLU networks.One particular conclusion is that: While the loss function of linear networks has no spurious local minimum, the loss function of one-hidden-layer nonlinear networks with ReLU activation function does have local minimum that is not global minimum.", "target": ["Forniamo forme analitiche necessarie e sufficienti per i punti critici delle loss function quadrate per varie reti neurali, e sfruttiamo le forme analitiche per caratterizzare le proprietà del landscape per le loss function di queste reti neurali."]} +{"source": "The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain.One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways.To address this “weight transport problem” (Grossberg, 1987), two biologically-plausible algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax BP’s weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets.However, a recent study by Bartunov et al. (2018) finds that although feedback alignment (FA) and some variants of target-propagation (TP) perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet.Here, we additionally evaluate the sign-symmetry (SS) algorithm (Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights do not share magnitudes but share signs.We examined the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet; RetinaNet for MS COCO).Surprisingly, networks trained with sign-symmetry can attain classification performance approaching that of BP-trained networks.These results complement the study by Bartunov et al. (2018) and establish a new benchmark for future biologically-plausible learning algorithms on more difficult datasets and more complex architectures.", "target": ["Gli algoritmi di apprendimento biologicamente plausibili, in particolare la simmetria dei segni, funzionano bene su ImageNet"]} +{"source": "We introduce the 2-simplicial Transformer, an extension of the Transformer which includes a form of higher-dimensional attention generalising the dot-product attention, and uses this attention to update entity representations with tensor products of value vectors.We show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning.", "target": ["Introduciamo il 2-simplicial Transformer e mostriamo che questa architettura è un utile bias induttivo per il ragionamento logico nel contesto del deep reinforcement learning."]} +{"source": "We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity to error propagation.Our proposed tensor recurrent architecture addresses these issues by learning the nonlinear dynamics directly using higher order moments and high-order state transition functions.Furthermore, we decompose the higher-order structure using the tensor-train (TT) decomposition to reduce the number of parameters while preserving the model performance.We theoretically establish the approximation properties of Tensor-Train RNNs for general sequence inputs, and such guarantees are not available for usual RNNs.We also demonstrate significant long-term prediction improvements over general RNN and LSTM architectures on a range of simulated environments with nonlinear dynamics, as well on real-world climate and traffic data.", "target": ["Previsione accurata su orizzonti temporali molto lunghi usando RNN tensor-train"]} +{"source": "Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret.We propose a variational message-passing algorithm for variational inference in such models.We make three contributions.First, we propose structured inference networks that incorporate the structure of the graphical model in the inference network of variational auto-encoders (VAE).Second, we establish conditions under which such inference networks enable fast amortized inference similar to VAE.Finally, we derive a variational message passing algorithm to perform efficient natural-gradient inference while retaining the efficiency of the amortized inference.By simultaneously enabling structured, amortized, and natural-gradient inference for deep structured models, our method simplifies and generalizes existing methods.", "target": ["Proponiamo un algoritmo di message-passing variazionale per modelli che contengono sia il deep model che il graphical model probabilistico."]} +{"source": "Modern deep neural networks have a large amount of weights, which make them difficult to deploy on computation constrained devices such as mobile phones.One common approach to reduce the model size and computational cost is to use low-rank factorization to approximate a weight matrix.However, performing standard low-rank factorization with a small rank can hurt the model expressiveness and significantly decrease the performance.In this work, we propose to use a mixture of multiple low-rank factorizations to model a large weight matrix, and the mixture coefficients are computed dynamically depending on its input.We demonstrate the effectiveness of the proposed approach on both language modeling and image classification tasks.Experiments show that our method not only improves the computation efficiency but also maintains (sometimes outperforms) its accuracy compared with the full-rank counterparts.", "target": ["Una semplice modifica alla fattorizzazione low-rank che migliora le prestazioni (sia in task con immagini sia testuali) pur essendo compatta."]} +{"source": "Deep learning training accesses vast amounts of data at high velocity, posing challenges for datasets retrieved over commodity networks and storage devices.We introduce a way to dynamically reduce the overhead of fetching and transporting training data with a method we term Progressive Compressed Records (PCRs).PCRs deviate from previous formats by leveraging progressive compression to split each training example into multiple examples of increasingly higher fidelity, without adding to the total data size.Training examples of similar fidelity are grouped together, which reduces both the system overhead and data bandwidth needed to train a model.We show that models can be trained on aggressively compressed representations of the training data and still retain high accuracy, and that PCRs can enable a 2x speedup on average over baseline formats using JPEG compression.Our results hold across deep learning architectures for a wide range of datasets: ImageNet, HAM10000, Stanford Cars, and CelebA-HQ.", "target": ["Proponiamo un formato di dati semplice, generale ed efficiente dal punto di vista dello spazio per accelerare il training del deep learning, permettendo che la fedeltà del sample sia selezionata dinamicamente al momento del training"]} +{"source": "It is fundamental and challenging to train robust and accurate Deep Neural Networks (DNNs) when semantically abnormal examples exist.Although great progress has been made, there is still one crucial research question which is not thoroughly explored yet: What training examples should be focused and how much more should they be emphasised to achieve robust learning?In this work, we study this question and propose gradient rescaling (GR) to solve it.GR modifies the magnitude of logit vector’s gradient to emphasise on relatively easier training data points when noise becomes more severe, which functions as explicit emphasis regularisation to improve the generalisation performance of DNNs.Apart from regularisation, we connect GR to examples weighting and designing robust loss functions.We empirically demonstrate that GR is highly anomaly-robust and outperforms the state-of-the-art by a large margin, e.g., increasing 7% on CIFAR100 with 40% noisy labels.It is also significantly superior to standard regularisers in both clean and abnormal settings.Furthermore, we present comprehensive ablation studies to explore the behaviours of GR under different cases, which is informative for applying GR in real-world scenarios.", "target": ["APPRENDIMENTO ROBUSTO DELLA RAPPRESENTAZIONE DISCRIMINATIVA ATTRAVERSO IL RESCALING DEL GRADIENTE: UNA PROSPETTIVA SULL' EMPHASIS REGULARISATION"]} +{"source": "Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images.In most applications, GAN models share two aspects in common.On the one hand, GANs training involves solving a challenging saddle point optimization problem, interpreted as an adversarial game between a generator and a discriminator functions.On the other hand, the generator and the discriminator are parametrized in terms of deep convolutional neural networks.The goal of this paper is to disentangle the contribution of these two factors to the success of GANs.In particular, we introduce Generative Latent Optimization (GLO), a framework to train deep convolutional generators without using discriminators, thus avoiding the instability of adversarial optimization problems.Throughout a variety of experiments, we show that GLO enjoys many of the desirable properties of GANs: learning from large data, synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with noise vectors.", "target": ["Le GAN hanno successo grazie all'adversarial training o all'uso di ConvNet? Mostriamo che un generatore ConvNet addestrato con una semplice loss di ricostruzione e noise vector learnable porta molte delle proprietà desiderabili di una GAN."]} +{"source": "In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN.Different from common forests with deterministic routings, a probabilistic routing variant is used in our innovated random-forest kernel, which is possible to merge with the CNN frameworks.Our proposed random-forest kernel has the following advantages: From the perspective of random forest, the output of GAN discriminator can be viewed as feature inputs to the forest, where each tree gets access to merely a fraction of the features, and thus the entire forest benefits from ensemble learning.In the aspect of kernel method, random-forest kernel is proved to be characteristic, and therefore suitable for the MMD structure.Besides, being an asymmetric kernel, our random-forest kernel is much more flexible, in terms of capturing the differences between distributions.Sharing the advantages of CNN, kernel method, and ensemble learning, our random-forest kernel based MMD GAN obtains desirable empirical performances on CIFAR-10, CelebA and LSUN bedroom data sets.Furthermore, for the sake of completeness, we also put forward comprehensive theoretical analysis to support our experimental results.", "target": ["Equipaggiare le MMD GAN con un nuovo random-forest kernel."]} +{"source": "Reinforcement learning in an actor-critic setting relies on accurate value estimates of the critic.However, the combination of function approximation, temporal difference (TD) learning and off-policy training can lead to an overestimating value function.A solution is to use Clipped Double Q-learning (CDQ), which is used in the TD3 algorithm and computes the minimum of two critics in the TD-target. We show that CDQ induces an underestimation bias and propose a new algorithm that accounts for this by using a weighted average of the target from CDQ and the target coming from a single critic.The weighting parameter is adjusted during training such that the value estimates match the actual discounted return on the most recent episodes and by that it balances over- and underestimation.Empirically, we obtain more accurate value estimates and demonstrate state of the art results on several OpenAI gym tasks.", "target": ["Un metodo per critic estimate più accurate nel reinforcement learning."]} +{"source": "We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos.As part of this framework, we construct ImageNet-Vid-Robust, a human-expert--reviewed dataset of 22,668 images grouped into 1,145 sets of perceptually similar images derived from frames in the ImageNet Video Object Detection dataset.We evaluate a diverse array of classifiers trained on ImageNet, including models trained for robustness, and show a median classification accuracy drop of 16\\%.Additionally, we evaluate the Faster R-CNN and R-FCN models for detection, and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points.Our analysis shows that natural perturbations in the real world are heavily problematic for current CNNs, posing a significant challenge to their deployment in safety-critical environments that require reliable, low-latency predictions.", "target": ["Introduciamo un framework sistematico per quantificare la robustezza dei classificatori alle perturbazioni naturali delle immagini presenti nei video."]} +{"source": "Structured tabular data is the most commonly used form of data in industry according to a Kaggle ML and DS Survey.Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data.The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach.In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embedding to address the problem of classification on tabular data.For each input of tabular data, the features are first projected into two-dimensional embedding like an image, and then this image is fed into fine-tuned ImageNet CNN models for classification.Experimental results have shown that the proposed SuperTML method have achieved state-of-the-art results on both large and small datasets.", "target": ["Deep learning per l'apprendimento automatico di dati tabulari strutturati usando un modello CNN pre-addestrato da ImageNet."]} +{"source": "Learning rich representations from predictive learning without labels has been a longstanding challenge in the field of machine learning.Generative pre-training has so far not been as successful as contrastive methods in modeling representations of raw images.In this paper, we propose a neural architecture for self-supervised representation learning on raw images called the PatchFormer which learns to model spatial dependencies across patches in a raw image.Our method learns to model the conditional probability distribution of missing patches given the context of surrounding patches.We evaluate the utility of the learned representations by fine-tuning the pre-trained model on low data-regime classification tasks.Specifically, we benchmark our model on semi-supervised ImageNet classification which has become a popular benchmark recently for semi-supervised and self-supervised learning methods.Our model is able to achieve 30.3% and 65.5% top-1 accuracies when trained only using 1% and 10% of the labels on ImageNet showing the promise for generative pre-training methods.", "target": ["La decodifica dei pixel può ancora funzionare per representation learning sulle immagini"]} +{"source": "Adaptive regularization methods pre-multiply a descent direction by a preconditioning matrix.Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive.We show how to modify full-matrix adaptive regularization in order to make it practical and effective.We also provide novel theoretical analysisfor adaptive regularization in non-convex optimization settings.The core of our algorithm, termed GGT, consists of efficient inverse computation of square roots of low-rank matrices.Our preliminary experiments underscore improved convergence rate of GGT across a variety of synthetic tasks and standard deep learning benchmarks.", "target": ["AdaGrad/Adam full-matrix, veloce e truly scalable, con la teoria per l'ottimizzazione adattativa stocastica non convessa"]} +{"source": "Dialogue systems require a great deal of different but complementary expertise to assist, inform, and entertain humans.For example, different domains (e.g., restaurant reservation, train ticket booking) of goal-oriented dialogue systems can be viewed as different skills, and so does ordinary chatting abilities of chit-chat dialogue systems.In this paper, we propose to learn a dialogue system that independently parameterizes different dialogue skills, and learns to select and combine each of them through Attention over Parameters (AoP).The experimental results show that this approach achieves competitive performance on a combined dataset of MultiWOZ (Budzianowski et al., 2018), In-Car Assistant (Eric et al.,2017), and Persona-Chat (Zhang et al., 2018).Finally, we demonstrate that each dialogue skill is effectively learned and can be combined with other skills to produce selective responses.", "target": ["In questo articolo, proponiamo di imparare un dialogue system che parametrizza indipendentemente diverse dialogue skill, e impara a selezionare e combinare ciascuna di esse attraverso l'Attention sui Parametri."]} +{"source": "Model distillation aims to distill the knowledge of a complex model into a simpler one.In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one.The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data.For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to the original performance, given a fixed network initialization.We evaluate our method in various initialization settings. Experiments on multiple datasets, MNIST, CIFAR10, PASCAL-VOC, and CUB-200, demonstrate the ad-vantage of our approach compared to alternative methods. Finally, we include a real-world application of dataset distillation to the continual learning setting: we show that storing distilled images as episodic memory of previous tasks can alleviate forgetting more effectively than real images.", "target": ["Proponiamo di distillare un grande dataset in un piccolo dataset sintetico che possono addestrare reti vicine alle prestazioni originali."]} +{"source": "We relate the minimax game of generative adversarial networks (GANs) to finding the saddle points of the Lagrangian function for a convex optimization problem, where the discriminator outputs and the distribution of generator outputs play the roles of primal variables and dual variables, respectively.This formulation shows the connection between the standard GAN training process and the primal-dual subgradient methods for convex optimization.The inherent connection does not only provide a theoretical convergence proof for training GANs in the function space, but also inspires a novel objective function for training.The modified objective function forces the distribution of generator outputs to be updated along the direction according to the primal-dual subgradient methods.A toy example shows that the proposed method is able to resolve mode collapse, which in this case cannot be avoided by the standard GAN or Wasserstein GAN.Experiments on both Gaussian mixture synthetic data and real-world image datasets demonstrate the performance of the proposed method on generating diverse samples.", "target": ["Proponiamo un metodo primale-duale con subgradient per il training di GAN e questo metodo allevia efficacemente il mode collapse."]} +{"source": "Specifying reward functions is difficult, which motivates the area of reward inference: learning rewards from human behavior.The starting assumption in the area is that human behavior is optimal given the desired reward function, but in reality people have many different forms of irrationality, from noise to myopia to risk aversion and beyond.This fact seems like it will be strictly harmful to reward inference: it is already hard to infer the reward from rational behavior, and noise and systematic biases make actions have less direct of a relationship to the reward.Our insight in this work is that, contrary to expectations, irrationality can actually help rather than hinder reward inference.For some types and amounts of irrationality, the expert now produces more varied policies compared to rational behavior, which help disambiguate among different reward parameters -- those that otherwise correspond to the same rational behavior.We put this to the test in a systematic analysis of the effect of irrationality on reward inference.We start by covering the space of irrationalities as deviations from the Bellman update, simulate expert behavior, and measure the accuracy of inference to contrast the different types and study the gains and losses.We provide a mutual information-based analysis of our findings, and wrap up by discussing the need to accurately model irrationality, as well as to what extent we might expect (or be able to train) real people to exhibit helpful irrationalities when teaching rewards to learners.", "target": ["Troviamo che l'irrazionalità di un dimostratore esperto può aiutare un learner ad indurre le sue preferenze."]} +{"source": "Natural Language Processing models lack a unified approach to robustness testing.In this paper we introduce WildNLP - a framework for testing model stability in a natural setting where text corruptions such as keyboard errors or misspelling occur.We compare robustness of models from 4 popular NLP tasks: Q&A, NLI, NER and Sentiment Analysis by testing their performance on aspects introduced in the framework.In particular, we focus on a comparison between recent state-of-the- art text representations and non-contextualized word embeddings.In order to improve robust- ness, we perform adversarial training on se- lected aspects and check its transferability to the improvement of models with various cor- ruption types.We find that the high perfor- mance of models does not ensure sufficient robustness, although modern embedding tech- niques help to improve it.We release cor- rupted datasets and code for WildNLP frame- work for the community.", "target": ["Confrontiamo la robustezza dei modelli di 4 popolari task NLP: Q&A, NLI, NER e Sentiment Analysis testando le loro prestazioni su input perturbati."]} +{"source": "Training generative models like Generative Adversarial Network (GAN) is challenging for noisy data.A novel curriculum learning algorithm pertaining to clustering is proposed to address this issue in this paper.The curriculum construction is based on the centrality of underlying clusters in data points. The data points of high centrality takes priority of being fed into generative models during training.To make our algorithm scalable to large-scale data, the active set is devised, in the sense that every round of training proceeds only on an active subset containing a small fraction of already trained data and the incremental data of lower centrality.Moreover, the geometric analysis is presented to interpret the necessity of cluster curriculum for generative models.The experiments on cat and human-face data validate that our algorithm is able to learn the optimal generative models (e.g. ProGAN) with respect to specified quality metrics for noisy data.An interesting finding is that the optimal cluster curriculum is closely related to the critical point of the geometric percolation process formulated in the paper.", "target": ["Un nuovo algoritmo di curriculum learning basato su cluster viene proposto per risolvere il training robusto di modelli generativi."]} +{"source": "Backdoor attacks aim to manipulate a subset of training data by injecting adversarial triggers such that machine learning models trained on the tampered dataset will make arbitrarily (targeted) incorrect prediction on the testset with the same trigger embedded.While federated learning (FL) is capable of aggregating information provided by different parties for training a better model, its distributed learning methodology and inherently heterogeneous data distribution across parties may bring new vulnerabilities.In addition to recent centralized backdoor attacks on FL where each party embeds the same global trigger during training, we propose the distributed backdoor attack (DBA) --- a novel threat assessment framework developed by fully exploiting the distributed nature of FL.DBA decomposes a global trigger pattern into separate local patterns and embed them into the training set of different adversarial parties respectively.Compared to standard centralized backdoors, we show that DBA is substantially more persistent and stealthy against FL on diverse datasets such as finance and image data.We conduct extensive experiments to show that the attack success rate of DBA is significantly higher than centralized backdoors under different settings.Moreover, we find that distributed attacks are indeed more insidious, as DBA can evade two state-of-the-art robust FL algorithms against centralized backdoors.We also provide explanations for the effectiveness of DBA via feature visual interpretation and feature importance ranking.To further explore the properties of DBA, we test the attack performance by varying different trigger factors, including local trigger variations (size, gap, and location), scaling factor in FL, data distribution, and poison ratio and interval.Our proposed DBA and thorough evaluation results shed lights on characterizing the robustness of FL.", "target": ["Abbiamo proposto un nuovo attacco backdoor distribuito sul federated learning e dimostriamo che non solo è più efficace rispetto agli attacchi centralizzati standard, ma anche più difficile da difendere con i metodi FL robusti esistenti"]} +{"source": "Graph networks have recently attracted considerable interest, and in particular in the context of semi-supervised learning.These methods typically work by generating node representations that are propagated throughout a given weighted graph.Here we argue that for semi-supervised learning, it is more natural to consider propagating labels in the graph instead.Towards this end, we propose a differentiable neural version of the classic Label Propagation (LP) algorithm.This formulation can be used for learning edge weights, unlike other methods where weights are set heuristically.Starting from a layer implementing a single iteration of LP, we proceed by adding several important non-linear steps that significantly enhance the label-propagating mechanism.Experiments in two distinct settings demonstrate the utility of our approach.", "target": ["Rete neurale per l'apprendimento semi-supervised basato sui grafi; rivisita i classici e propaga le label piuttosto che le rappresentazioni delle feature"]} +{"source": "Neural architecture search (NAS) has made rapid progress incomputervision,wherebynewstate-of-the-artresultshave beenachievedinaseriesoftaskswithautomaticallysearched neural network (NN) architectures.In contrast, NAS has not made comparable advances in natural language understanding (NLU).Corresponding to encoder-aggregator meta architecture of typical neural networks models for NLU tasks (Gong et al. 2018), we re-define the search space, by splittingitinto twoparts:encodersearchspace,andaggregator search space.Encoder search space contains basic operations such as convolutions, RNNs, multi-head attention and its sparse variants, star-transformers.Dynamic routing is included in the aggregator search space, along with max (avg) pooling and self-attention pooling.Our search algorithm is then fulfilled via DARTS, a differentiable neural architecture search framework.We progressively reduce the search space every few epochs, which further reduces the search time and resource costs.Experiments on five benchmark data-sets show that, the new neural networks we generate can achieve performances comparable to the state-of-the-art models that does not involve language model pre-training.", "target": ["Neural Architecture Search per una serie di task di Natural Language Understanding. Design dello spazio di ricerca per i task di NLU. Si applica la ricerca dell'architettura differenziabile per scoprire nuovi modelli"]} +{"source": "Network embedding (NE) methods aim to learn low-dimensional representations of network nodes as vectors, typically in Euclidean space.These representations are then used for a variety of downstream prediction tasks.Link prediction is one of the most popular choices for assessing the performance of NE methods.However, the complexity of link prediction requires a carefully designed evaluation pipeline to provide consistent, reproducible and comparable results.We argue this has not been considered sufficiently in recent works.The main goal of this paper is to overcome difficulties associated with evaluation pipelines and reproducibility of results.We introduce EvalNE, an evaluation framework to transparently assess and compare the performance of NE methods on link prediction.EvalNE provides automation and abstraction for tasks such as hyper-parameter tuning, model validation, edge sampling, computation of edge embeddings and model validation.The framework integrates efficient procedures for edge and non-edge sampling and can be used to easily evaluate any off-the-shelf embedding method.The framework is freely available as a Python toolbox.Finally, demonstrating the usefulness of EvalNE in practice, we conduct an empirical study in which we try to replicate and analyse experimental sections of several influential papers.", "target": ["In questo articolo introduciamo EvalNE, un toolbox Python per automatizzare la valutazione dei metodi di embedding di rete sulla predizione dei link garantendo la riproducibilità dei risultati."]} +{"source": "Deep learning models can be efficiently optimized via stochastic gradient descent, but there is little theoretical evidence to support this.A key question in optimization is to understand when the optimization landscape of a neural network is amenable to gradient-based optimization.We focus on a simple neural network two-layer ReLU network with two hidden units, and show that all local minimizers are global.This combined with recent work of Lee et al. (2017); Lee et al. (2016) show that gradient descent converges to the global minimizer.", "target": ["Garanzia di recovery della stochastic gradient descent con inizializzazione casuale per l'apprendimento di una rete neurale a due strati con due hidden unit, pesi a norma unitaria, funzioni di attivazione ReLU e input gaussiani."]} +{"source": "Dropout is a simple yet effective technique to improve generalization performance and prevent overfitting in deep neural networks (DNNs).In this paper, we discuss three novel observations about dropout to better understand the generalization of DNNs with rectified linear unit (ReLU) activations: 1) dropout is a smoothing technique that encourages each local linear model of a DNN to be trained on data points from nearby regions; 2) a constant dropout rate can result in effective neural-deactivation rates that are significantly different for layers with different fractions of activated neurons; and 3) the rescaling factor of dropout causes an inconsistency to occur between the normalization during training and testing conditions when batch normalization is also used. The above leads to three simple but nontrivial improvements to dropout resulting in our proposed method \"Jumpout.\"Jumpout samples the dropout rate using a monotone decreasing distribution (such as the right part of a truncated Gaussian), so the local linear model at each data point is trained, with high probability, to work better for data points from nearby than from more distant regions.Instead of tuning a dropout rate for each layer and applying it to all samples, jumpout moreover adaptively normalizes the dropout rate at each layer and every training sample/batch, so the effective dropout rate applied to the activated neurons are kept the same.Moreover, we rescale the outputs of jumpout for a better trade-off that keeps both the variance and mean of neurons more consistent between training and test phases, which mitigates the incompatibility between dropout and batch normalization.Compared to the original dropout, jumpout shows significantly improved performance on CIFAR10, CIFAR100, Fashion- MNIST, STL10, SVHN, ImageNet-1k, etc., while introducing negligible additional memory and computation costs.", "target": ["Jumpout applica tre semplici ma efficaci modifiche al dropout, basate su nuove conoscenze sulle prestazioni di generalizzazione della DNN con ReLU nelle regioni locali."]} +{"source": "Concerns about interpretability, computational resources, and principled inductive priors have motivated efforts to engineer sparse neural models for NLP tasks.If sparsity is important for NLP, might well-trained neural models naturally become roughly sparse?Using the Taxi-Euclidean norm to measure sparsity, we find that frequent input words are associated with concentrated or sparse activations, while frequent target words are associated with dispersed activations but concentrated gradients.We find that gradients associated with function words are more concentrated than the gradients of content words, even controlling for word frequency.", "target": ["Studiamo l'emergere naturale della sparsità nelle attivazioni e nei gradienti per alcuni layer di un language model LSTM denso, nel corso del training."]} +{"source": "The integration of a Knowledge Base (KB) into a neural dialogue agent is one of the key challenges in Conversational AI.Memory networks has proven to be effective to encode KB information into an external memory to thus generate more fluent and informed responses.Unfortunately, such memory becomes full of latent representations during training, so the most common strategy is to overwrite old memory entries randomly. In this paper, we question this approach and provide experimental evidence showing that conventional memory networks generate many redundant latent vectors resulting in overfitting and the need for larger memories.We introduce memory dropout as an automatic technique that encourages diversity in the latent space by1) Aging redundant memories to increase their probability of being overwritten during training2) Sampling new memories that summarize the knowledge acquired by redundant memories.This technique allows us to incorporate Knowledge Bases to achieve state-of-the-art dialogue generation in the Stanford Multi-Turn Dialogue dataset.Considering the same architecture, its use provides an improvement of +2.2 BLEU points for the automatic generation of responses and an increase of +8.1% in the recognition of named entities.", "target": ["Le memory network convenzionali generano molti vettori latenti ridondanti con conseguente overfitting e la necessità di memorie più grandi. Introduciamo il dropout della memoria come una tecnica automatica che incoraggia la diversità nello spazio latente."]} +{"source": "Su-Boyd-Candes (2014) made a connection between Nesterov's method and an ordinary differential equation (ODE). We show if a Hessian damping term is added to the ODE from Su-Boyd-Candes (2014), then Nesterov's method arises as a straightforward discretization of the modified ODE.Analogously, in the strongly convex case, a Hessian damping term is added to Polyak's ODE, which is then discretized to yield Nesterov's method for strongly convex functions. Despite the Hessian term, both second order ODEs can be represented as first order systems.Established Liapunov analysis is used to recover the accelerated rates of convergence in both continuous and discrete time. Moreover, the Liapunov analysis can be extended to the case of stochastic gradients which allows the full gradient case to be considered as a special case of the stochastic case. The result is a unified approach to convex acceleration in both continuous and discrete time and in both the stochastic and full gradient cases.", "target": ["Il metodo di Nesterov si presenta come una discretizzazione diretta di un'ODE diversa da quella di Su-Boyd-Candes e dimostriamo l'accelerazione del caso stocastico"]} +{"source": "We propose learning to transfer learn (L2TL) to improve transfer learning on a target dataset by judicious extraction of information from a source dataset.L2TL considers joint optimization of vastly-shared weights between models for source and target tasks, and employs adaptive weights for scaling of constituent losses.The adaptation of the weights is based on reinforcement learning, guided with a performance metric on the target validation set.We demonstrate state-of-the-art performance of L2TL given fixed models, consistently outperforming fine-tuning baselines on various datasets.In the regimes of small-scale target datasets and significant label mismatch between source and target datasets, L2TL outperforms previous work by an even larger margin.", "target": ["Proponiamo di imparare a trasferire l'apprendimento (L2TL) per migliorare il transfer learning su un dataset di destinazione attraverso un'attenta estrazione di informazioni da un dataset di origine."]} +{"source": "In many partially observable scenarios, Reinforcement Learning (RL) agents must rely on long-term memory in order to learn an optimal policy.We demonstrate that using techniques from NLP and supervised learning fails at RL tasks due to stochasticity from the environment and from exploration.Utilizing our insights on the limitations of traditional memory methods in RL, we propose AMRL, a class of models that can learn better policies with greater sample efficiency and are resilient to noisy inputs.Specifically, our models use a standard memory module to summarize short-term context, and then aggregate all prior states from the standard model without respect to order.We show that this provides advantages both in terms of gradient decay and signal-to-noise ratio over time.Evaluating in Minecraft and maze environments that test long-term memory, we find that our model improves average return by 19% over a baseline that has the same number of parameters and by 9% over a stronger baseline that has far more parameters.", "target": ["In Deep RL, le funzioni invarianti rispetto all'ordine possono essere utilizzate insieme ai moduli di memoria standard per migliorare il gradient decay e la resilienza al rumore."]} +{"source": "Optimization on manifold has been widely used in machine learning, to handle optimization problems with constraint.Most previous works focus on the case with a single manifold.However, in practice it is quite common that the optimization problem involves more than one constraints, (each constraint corresponding to one manifold).It is not clear in general how to optimize on multiple manifolds effectively and provably especially when the intersection of multiple manifolds is not a manifold or cannot be easily calculated.We propose a unified algorithm framework to handle the optimization on multiple manifolds.Specifically, we integrate information from multiple manifolds and move along an ensemble direction by viewing the information from each manifold as a drift and adding them together.We prove the convergence properties of the proposed algorithms.We also apply the algorithms into training neural network with batch normalization layers and achieve preferable empirical results.", "target": ["Questo articolo introduce un algoritmo per gestire un problema di ottimizzazione con vincoli multipli sotto la visione di manifold."]} +{"source": "It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponentially large number of bins over which policies would have to be learned.In this paper, we draw inspiration from the recent success of sequence-to-sequence models for structured prediction problems to develop policies over discretized spaces.Central to this method is the realization that complex functions over high dimensional spaces can be modeled by neural networks that predict one dimension at a time.Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions.With this parameterization, it is possible to both leverage the compositional structure of action spaces during learning, as well as compute maxima over action spaces (approximately).On a simple example task we demonstrate empirically that our method can perform global search, which effectively gets around the local optimization issues that plague DDPG.We apply the technique to off-policy (Q-learning) methods and show that our method can achieve the state-of-the-art for off-policy methods on several continuous control tasks.", "target": ["Un metodo per fare Q-learning su spazi d'azione continui prevedendo una sequenza di azioni 1-D discretizzate."]} +{"source": "Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments.However, due to estimation error, rollouts in the learned model, especially those of long horizon, fail to match the ones in real-world environments.This mismatching has seriously impacted the sample complexity of MBRL.The phenomenon can be attributed to the fact that previous works employ supervised learning to learn the one-step transition models, which has inherent difficulty ensuring the matching of distributions from multi-step rollouts.Based on the claim, we propose to learn the synthesized model by matching the distributions of multi-step rollouts sampled from the synthesized model and the real ones via WGAN.We theoretically show that matching the two can minimize the difference of cumulative rewards between the real transition and the learned one.Our experiments also show that the proposed model imitation method outperforms the state-of-the-art in terms of sample complexity and average return.", "target": ["Il nostro metodo incorpora WGAN per ottenere occupancy measure matching per transition learning."]} +{"source": "Batch Normalization (BN) and its variants have seen widespread adoption in the deep learning community because they improve the training of deep neural networks.Discussions of why this normalization works so well remain unsettled. We make explicit the relationship between ordinary least squares and partial derivatives computed when back-propagating through BN.We recast the back-propagation of BN as a least squares fit, which zero-centers and decorrelates partial derivatives from normalized activations.This view, which we term {\\em gradient-least-squares}, is an extensible and arithmetically accurate description of BN.To further explore this perspective, we motivate, interpret, and evaluate two adjustments to BN.", "target": ["La normalizzazione gaussiana esegue un adattamento ai minimi quadrati durante la back-propagation, che azzera e decorrela le derivate parziali dalle attivazioni normalizzate."]} +{"source": "Batch Normalization (BN) has become a cornerstone of deep learning across diverse architectures, appearing to help optimization as well as generalization.While the idea makes intuitive sense, theoretical analysis of its effectiveness has been lacking.Here theoretical support is provided for one of its conjectured properties, namely, the ability to allow gradient descent to succeed with less tuning of learning rates.It is shown that even if we fix the learning rate of scale-invariant parameters (e.g., weights of each layer with BN) to a constant (say, 0.3), gradient descent still approaches a stationary point (i.e., a solution where gradient is zero) in the rate of T^{−1/2} in T iterations, asymptotically matching the best bound for gradient descent with well-tuned learning rates.A similar result with convergence rate T^{−1/4} is also shown for stochastic gradient descent.", "target": ["Diamo un'analisi teorica della capacità della batch normalization di sintonizzare automaticamente i learning rate, nel contesto della ricerca di punti stazionari per un obiettivo di deep learning."]} +{"source": "Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale.We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity and fidelity than previous work. Our proposed model, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator.We evaluate on the related tasks of video synthesis and video prediction, and achieve new state-of-the-art Fréchet Inception Distance for prediction for Kinetics-600, as well as state-of-the-art Inception Score for synthesis on the UCF-101 dataset, alongside establishing a strong baseline for synthesis on Kinetics-600.", "target": ["Proponiamo DVD-GAN, un modello generativo di video di grandi dimensioni che è allo stato dell'arte su diversi task e produce video altamente complessi quando addestrato su grandi dataset del mondo reale."]} +{"source": "Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated.In this work, we introduce Neural Process Networks to understand procedural text through (neural) simulation of action dynamics. Our model complements existing memory architectures with dynamic entity tracking by explicitly modeling actions as state transformers.The model updates the states of the entities by executing learned action operators.Empirical results demonstrate that our proposed model can reason about the unstated causal effects of actions, allowing it to provide more accurate contextual information for understanding and generating procedural text, all while offering more interpretable internal representations than existing alternatives.", "target": ["Proponiamo una nuova architettura di memoria ricorrente che può tracciare i cambiamenti di stato di common sense nelle entità simulando gli effetti causali delle azioni."]} +{"source": "There has been a recent trend in training neural networks to replace data structures that have been crafted by hand, with an aim for faster execution, better accuracy, or greater compression. In this setting, a neural data structure is instantiated by training a network over many epochs of its inputs until convergence.In many applications this expensive initialization is not practical, for example streaming algorithms --- where inputs are ephemeral and can only be inspected a small number of times. In this paper we explore the learning of approximate set membership over a stream of data in one-shot via meta-learning.We propose a novel memory architecture, the Neural Bloom Filter, which we show to be more compressive than Bloom Filters and several existing memory-augmented neural networks in scenarios of skewed data or structured sets.", "target": ["Studiamo l'efficienza spaziale delle reti neurali memory-augmented durante il learning di set membership."]} +{"source": "We leverage recent insights from second-order optimisation for neural networks to construct a Kronecker factored Laplace approximation to the posterior over the weights of a trained network.Our approximation requires no modification of the training procedure, enabling practitioners to estimate the uncertainty of their models currently used in production without having to retrain them.We extensively compare our method to using Dropout and a diagonal Laplace approximation for estimating the uncertainty of a network.We demonstrate that our Kronecker factored method leads to better uncertainty estimates on out-of-distribution data and is more robust to simple adversarial attacks.Our approach only requires calculating two square curvature factor matrices for each layer.Their size is equal to the respective square of the input and output size of the layer, making the method efficient both computationally and in terms of memory usage.We illustrate its scalability by applying it to a state-of-the-art convolutional network architecture.", "target": ["Costruiamo un'approssimazione di Laplace con fattore di Kronecker per le reti neurali che porta ad un'efficiente distribuzione normale della matrice dei pesi."]} +{"source": "Spectral embedding is a popular technique for the representation of graph data.Several regularization techniques have been proposed to improve the quality of the embedding with respect to downstream tasks like clustering.In this paper, we explain on a simple block model the impact of the complete graph regularization, whereby a constant is added to all entries of the adjacency matrix.Specifically, we show that the regularization forces the spectral embedding to focus on the largest blocks, making the representation less sensitive to noise or outliers.We illustrate these results on both on both synthetic and real data, showing how regularization improves standard clustering scores.", "target": ["La regolarizzazione dei grafi costringe l'embedding spettrale a concentrarsi sui cluster più grandi, rendendo la rappresentazione meno sensibile al rumore."]} +{"source": "The exposure bias problem refers to the training-inference discrepancy caused by teacher forcing in maximum likelihood estimation (MLE) training for auto-regressive neural network language models (LM).It has been regarded as a central problem for natural language generation (NLG) model training.Although a lot of algorithms have been proposed to avoid teacher forcing and therefore to alleviate exposure bias, there is little work showing how serious the exposure bias problem is.In this work, we first identify the auto-recovery ability of MLE-trained LM, which casts doubt on the seriousness of exposure bias.We then develop a precise, quantifiable definition for exposure bias.However, according to our measurements in controlled experiments, there's only around 3% performance gain when the training-inference discrepancy is completely removed.Our results suggest the exposure bias problem could be much less serious than it is currently assumed to be.", "target": ["Mostriamo che il bias di esposizione potrebbe essere molto meno grave di quello che si presume attualmente per il training MLE di LM."]} +{"source": "The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks.Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neural network agents on referential communication games.We extend previous work, in which agents were trained in symbolic environments, by developing agents which are able to learn from raw pixel data, a more challenging and realistic input representation.We find that the degree of structure found in the input data affects the nature of the emerged protocols, and thereby corroborate the hypothesis that structured compositional language is most likely to emerge when agents perceive the world as being structured.", "target": ["Uno studio controllato del ruolo degli ambienti rispetto alle proprietà nei protocolli di comunicazione emergenti."]} +{"source": "For understanding generic documents, information like font sizes, column layout, and generally the positioning of words may carry semantic information that is crucial for solving a downstream document intelligence task.Our novel BERTgrid, which is based on Chargrid by Katti et al. (2018), represents a document as a grid of contextualized word piece embedding vectors, thereby making its spatial structure and semantics accessible to the processing neural network.The contextualized embedding vectors are retrieved from a BERT language model.We use BERTgrid in combination with a fully convolutional network on a semantic instance segmentation task for extracting fields from invoices.We demonstrate its performance on tabulated line item and document header field extraction.", "target": ["Rappresentazione del documento basata su grid con vettori di embedding contestualizzati per documenti con layout 2D"]} +{"source": "Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers.However, an attacker is not usually able to directly modify another agent's observations.This might lead one to wonder: is it possible to attack an RL agent simply by choosing an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial?We demonstrate the existence of adversarial policies in zero-sum games between simulated humanoid robots with proprioceptive observations, against state-of-the-art victims trained via self-play to be robust to opponents.The adversarial policies reliably win against the victims but generate seemingly random and uncoordinated behavior.We find that these policies are more successful in high-dimensional environments, and induce substantially different activations in the victim policy network than when the victim plays against a normal opponent.Videos are available at https://attackingrl.github.io.", "target": ["Le policy di deep RL possono essere attaccate da altri agenti che compiono azioni in modo da creare osservazioni naturali che sono adversarial."]} +{"source": "GloVe and Skip-gram word embedding methods learn word vectors by decomposing a denoised matrix of word co-occurrences into a product of low-rank matrices.In this work, we propose an iterative algorithm for computing word vectors based on modeling word co-occurrence matrices with Generalized Low Rank Models.Our algorithm generalizes both Skip-gram and GloVe as well as giving rise to other embedding methods based on the specified co-occurrence matrix, distribution of co-occurences, and the number of iterations in the iterative algorithm.For example, using a Tweedie distribution with one iteration results in GloVe and using a Multinomial distribution with full-convergence mode results in Skip-gram.Experimental results demonstrate that multiple iterations of our algorithm improves results over the GloVe method on the Google word analogy similarity task.", "target": ["Presentiamo un nuovo algoritmo iterativo basato su modelli generalizzati di basso rango per il calcolo e l'interpretazione dei modelli di word embedding."]} +{"source": "Deterministic models are approximations of reality that are often easier to build and interpret than stochastic alternatives. Unfortunately, as nature is capricious, observational data can never be fully explained by deterministic models in practice. Observation and process noise need to be added to adapt deterministic models to behave stochastically, such that they are capable of explaining and extrapolating from noisy data.Adding process noise to deterministic simulators can induce a failure in the simulator resulting in no return value for certain inputs -- a property we describe as ``brittle.''We investigate and address the wasted computation that arises from these failures, and the effect of such failures on downstream inference tasks.We show that performing inference in this space can be viewed as rejection sampling, and train a conditional normalizing flow as a proposal over noise values such that there is a low probability that the simulator crashes, increasing computational efficiency and inference fidelity for a fixed sample budget when used as the proposal in an approximate inference algorithm.", "target": ["Impariamo un flusso autoregressivo condizionato per proporre perturbazioni che non inducono il fallimento del simulatore, migliorando le prestazioni di inferenza."]} +{"source": "Multi-hop question answering requires models to gather information from different parts of a text to answer a question.Most current approaches learn to address this task in an end-to-end way with neural networks, without maintaining an explicit representation of the reasoning process.We propose a method to extract a discrete reasoning chain over the text, which consists of a series of sentences leading to the answer.We then feed the extracted chains to a BERT-based QA model to do final answer prediction.Critically, we do not rely on gold annotated chains or ``supporting facts:'' at training time, we derive pseudogold reasoning chains using heuristics based on named entity recognition and coreference resolution.Nor do we rely on these annotations at test time, as our model learns to extract chains from raw text alone. We test our approach on two recently proposed large multi-hop question answering datasets: WikiHop and HotpotQA, and achieve state-of-art performance on WikiHop and strong performance on HotpotQA.Our analysis shows the properties of chains that are crucial for high performance: in particular, modeling extraction sequentially is important, as is dealing with each candidate sentence in a context-aware way.Furthermore, human evaluation shows that our extracted chains allow humans to give answers with high confidence, indicating that these are a strong intermediate abstraction for this task.", "target": ["Miglioriamo il question answering che richiedono un ragionamento multi-hop estraendo una catena intermedia di frasi."]} +{"source": "Normalizing constant (also called partition function, Bayesian evidence, or marginal likelihood) is one of the central goals of Bayesian inference, yet most of the existing methods are both expensive and inaccurate.Here we develop a new approach, starting from posterior samples obtained with a standard Markov Chain Monte Carlo (MCMC).We apply a novel Normalizing Flow (NF) approach to obtain an analytic density estimator from these samples, followed by Optimal Bridge Sampling (OBS) to obtain the normalizing constant.We compare our method which we call Gaussianized Bridge Sampling (GBS) to existing methods such as Nested Sampling (NS) and Annealed Importance Sampling (AIS) on several examples, showing our method is both significantly faster and substantially more accurate than these methods, and comes with a reliable error estimation.", "target": ["Sviluppiamo un nuovo metodo per la stima della costante di normalizzazione (evidenza bayesiana) usando un Optimal Bridge Sampling e un nuovo Normalizing Flow, che dimostra di superare i metodi esistenti in termini di precisione e tempo di calcolo."]} +{"source": "We present a large-scale empirical study of catastrophic forgetting (CF) in modern Deep Neural Network (DNN) models that perform sequential (or: incremental) learning.A new experimental protocol is proposed that takes into account typical constraints encountered in application scenarios.As the investigation is empirical, we evaluate CF behavior on the hitherto largest number of visual classification datasets, from each of which we construct a representative number of Sequential Learning Tasks (SLTs) in close alignment to previous works on CF.Our results clearly indicate that there is no model that avoids CF for all investigated datasets and SLTs under application conditions.We conclude with a discussion of potential solutions and workarounds to CF, notably for the EWC and IMM models.", "target": ["Controlliamo i modelli DNN per la catastrophic forgetting usando un nuovo schema di valutazione che riflette le tipiche condizioni di applicazione, con risultati sorprendenti."]} +{"source": "Federated Learning (FL) refers to learning a high quality global model based on decentralized data storage, without ever copying the raw data.A natural scenario arises with data created on mobile phones by the activity of their users.Given the typical data heterogeneity in such situations, it is natural to ask how can the global model be personalized for every such device, individually.In this work, we point out that the setting of Model Agnostic Meta Learning (MAML), where one optimizes for a fast, gradient-based, few-shot adaptation to a heterogeneous distribution of tasks, has a number of similarities with the objective of personalization for FL.We present FL as a natural source of practical applications for MAML algorithms, and make the following observations.1) The popular FL algorithm, Federated Averaging, can be interpreted as a meta learning algorithm.2) Careful fine-tuning can yield a global model with higher accuracy, which is at the same time easier to personalize.However, solely optimizing for the global model accuracy yields a weaker personalization result.3) A model trained using a standard datacenter optimization method is much harder to personalize, compared to one trained using Federated Averaging, supporting the first claim.These results raise new questions for FL, MAML, and broader ML research.", "target": ["Federated Averaging è già un algoritmo di Meta Learning, mentre i metodi addestrati nel datacenter sono significativamente più difficili da personalizzare."]} +{"source": "Memorization of data in deep neural networks has become a subject of significant research interest. In this paper, we link memorization of images in deep convolutional autoencoders to downsampling through strided convolution. To analyze this mechanism in a simpler setting, we train linear convolutional autoencoders and show that linear combinations of training data are stored as eigenvectors in the linear operator corresponding to the network when downsampling is used. On the other hand, networks without downsampling do not memorize training data. We provide further evidence that the same effect happens in nonlinear networks. Moreover, downsampling in nonlinear networks causes the model to not only memorize just linear combinations of images, but individual training images. Since convolutional autoencoder components are building blocks of deep convolutional networks, we envision that our findings will shed light on the important phenomenon of memorization in over-parameterized deep networks.", "target": ["Identifichiamo il downsampling come un meccanismo per la memorizzazione negli autoencoder convoluzionali."]} +{"source": "Reinforcement learning provides a powerful and general framework for decisionmaking and control, but its application in practice is often hindered by the needfor extensive feature and reward engineering.Deep reinforcement learning methodscan remove the need for explicit engineering of policy or value features, butstill require a manually specified reward function.Inverse reinforcement learningholds the promise of automatic reward acquisition, but has proven exceptionallydifficult to apply to large, high-dimensional problems with unknown dynamics.Inthis work, we propose AIRL, a practical and scalable inverse reinforcement learningalgorithm based on an adversarial reward learning formulation that is competitivewith direct imitation learning algorithms.Additionally, we show that AIRL isable to recover portable reward functions that are robust to changes in dynamics,enabling us to learn policies even under significant variation in the environmentseen during training.", "target": ["Proponiamo un algoritmo di reinforcement learning inverso in grado di apprendere funzioni di reward che possono essere trasferite a nuovi ambienti non visti."]} +{"source": "We consider two questions at the heart of machine learning; how can we predict if a minimum will generalize to the test set, and why does stochastic gradient descent find minima that generalize well?Our work responds to \\citet{zhang2016understanding}, who showed deep neural networks can easily memorize randomly labeled training data, despite generalizing well on real labels of the same inputs.We show that the same phenomenon occurs in small linear models.These observations are explained by the Bayesian evidence, which penalizes sharp minima but is invariant to model parameterization.We also demonstrate that, when one holds the learning rate fixed, there is an optimum batch size which maximizes the test set accuracy.We propose that the noise introduced by small mini-batches drives the parameters towards minima whose evidence is large.Interpreting stochastic gradient descent as a stochastic differential equation, we identify the ``noise scale\" $g = \\epsilon (\\frac{N}{B} - 1) \\approx \\epsilon N/B$, where $\\epsilon$ is the learning rate, $N$ the training set size and $B$ the batch size.Consequently the optimum batch size is proportional to both the learning rate and the size of the training set, $B_{opt} \\propto \\epsilon N$.We verify these predictions empirically.", "target": ["La generalizzazione è fortemente correlata all'evidenza bayesiana, e il rumore del gradiente spinge SGD verso i minimi la cui evidenza è grande."]} +{"source": "In the industrial field, the positron annihilation is not affected by complex environment, and the gamma-ray photon penetration is strong, so the nondestructive detection of industrial parts can be realized.Due to the poor image quality caused by gamma-ray photon scattering, attenuation and short sampling time in positron process, we propose the idea of combining deep learning to generate positron images with good quality and clear details by adversarial nets.The structure of the paper is as follows: firstly, we encode to get the hidden vectors of medical CT images based on transfer Learning, and use PCA to extract positron image features.Secondly, we construct a positron image memory based on attention mechanism as a whole input to the adversarial nets which uses medical hidden variables as a query.Finally, we train the whole model jointly and update the input parameters until convergence.Experiments have proved the possibility of generating rare positron images for industrial non-destructive testing using countermeasure networks, and good imaging results have been achieved.", "target": ["reti adversarial, meccanismo di attenzione, immagini di positroni, scarsità di dati"]} +{"source": "We revisit the Recurrent Attention Model (RAM, Mnih et al. (2014)), a recurrent neural network for visual attention, from an active information sampling perspective. We borrow ideas from neuroscience research on the role of active information sampling in the context of visual attention and gaze (Gottlieb, 2018), where the author suggested three types of motives for active information sampling strategies.We find the original RAM model only implements one of them.We identify three key weakness of the original RAM and provide a simple solution by adding two extra terms on the objective function.The modified RAM1) achieves faster convergence,2) allows dynamic decision making per sample without loss of accuracy, and3) generalizes much better on longer sequence of glimpses which is not trained for, compared with the original RAM.", "target": ["Ispirato dalla ricerca sulle neuroscienze, risolve tre debolezze chiave del modello di attenzione ricorrente ampiamente citato aggiungendo semplicemente due termini alla funzione obiettivo."]} +{"source": "Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.However, a large quantity of labeled graphs is difficult to obtain, which significantly limit the true success of GNNs.Although active learning has been widely studied for addressing label-sparse issues with other data types like text, images, etc., how to make it effective over graphs is an open question for research. In this paper, we present the investigation on active learning with GNNs for node classification tasks. Specifically, we propose a new method, which uses node feature propagation followed by K-Medoids clustering of the nodes for instance selection in active learning.With a theoretical bound analysis we justify the design choice of our approach.In our experiments on four benchmark dataset, the proposed method outperforms other representative baseline methods consistently and significantly.", "target": ["Questo articolo introduce un algoritmo di active learning su grafi basato sul clustering."]} +{"source": "Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation.However, conditioning CNFs on signals of interest for conditional image generation and downstream predictive tasks is inefficient due to the high-dimensional latent code generated by the model, which needs to be of the same size as the input data.In this paper, we propose InfoCNF, an efficient conditional CNF that partitions the latent space into a class-specific supervised code and an unsupervised code that shared among all classes for efficient use of labeled information.Since the partitioning strategy (slightly) increases the number of function evaluations (NFEs), InfoCNF also employs gating networks to learn the error tolerances of its ordinary differential equation (ODE) solvers for better speed and performance.We show empirically that InfoCNF improves the test accuracy over the baseline while yielding comparable likelihood scores and reducing the NFEs on CIFAR10.Furthermore, applying the same partitioning strategy in InfoCNF on time-series data helps improve extrapolation performance.", "target": ["Proponiamo l'InfoCNF, un efficiente CNF condizionale che impiega gating networks per imparare le tolleranze di errore dei solutori di ODE"]} +{"source": "A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data.Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disentanglement, prediction, and other metrics.Instead, we develop an unsupervised meta-learning method that explicitly optimizes for the ability to learn a variety of tasks from small amounts of data.To do so, we construct tasks from unlabeled data in an automatic way and run meta-learning over the constructed tasks.Surprisingly, we find that, when integrated with meta-learning, relatively simple task construction mechanisms, such as clustering embeddings, lead to good performance on a variety of downstream, human-specified tasks.Our experiments across four image datasets indicate that our unsupervised meta-learning approach acquires a learning algorithm without any labeled data that is applicable to a wide range of downstream classification tasks, improving upon the embedding learned by four prior unsupervised learning methods.", "target": ["Un metodo di apprendimento unsupervised che utilizza il meta-learning per consentire l'apprendimento efficiente dei downstream task di classificazione delle immagini, superando i metodi allo stato dell'arte."]} +{"source": "Domain transfer is a exciting and challenging branch of machine learning because models must learn to smoothly transfer between domains, preserving local variations and capturing many aspects of variation without labels. However, most successful applications to date require the two domains to be closely related (ex. image-to-image, video-video), utilizing similar or shared networks to transform domain specific properties like texture, coloring, and line shapes. Here, we demonstrate that it is possible to transfer across modalities (ex. image-to-audio) by first abstracting the data with latent generative models and then learning transformations between latent spaces. We find that a simple variational autoencoder is able to learn a shared latent space to bridge between two generative models in an unsupervised fashion, and even between different types of models (ex. variational autoencoder and a generative adversarial network). We can further impose desired semantic alignment of attributes with a linear classifier in the shared latent space. The proposed variation autoencoder enables preserving both locality and semantic alignment through the transfer process, as shown in the qualitative and quantitative evaluations.Finally, the hierarchical structure decouples the cost of training the base generative models and semantic alignments, enabling computationally efficient and data efficient retraining of personalized mapping functions.", "target": ["VAE condizionale su spazi latenti di modelli generativi pre-trained che permette il transfer tra domini drasticamente diversi preservando la localizzazione e l'allineamento semantico."]} +{"source": "We propose Adversarial Inductive Transfer Learning (AITL), a method for addressing discrepancies in input and output spaces between source and target domains.AITL utilizes adversarial domain adaptation and multi-task learning to address these discrepancies.Our motivating application is pharmacogenomics where the goal is to predict drug response in patients using their genomic information.The challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines) and clinical datasets.Discrepancies exist between1) the genomic data of pre-clinical and clinical datasets (the input space), and2) the different measures of the drug response (the output space).To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies.Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.", "target": ["Un nuovo metodo di inductive transfer learning che impiega l'adversarial learning e il multi-task learning per affrontare la discrepanza nello spazio di input e output"]} +{"source": "Named entity recognition (NER) and relation extraction (RE) are two important tasks in information extraction and retrieval (IE & IR).Recent work has demonstrated that it is beneficial to learn these tasks jointly, which avoids the propagation of error inherent in pipeline-based systems and improves performance.However, state-of-the-art joint models typically rely on external natural language processing (NLP) tools, such as dependency parsers, limiting their usefulness to domains (e.g. news) where those tools perform well.The few neural, end-to-end models that have been proposed are trained almost completely from scratch.In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained language model.Because the bulk of our model's parameters are pre-trained and we eschew recurrence for self-attention, our model is fast to train.On 5 datasets across 3 domains, our model matches or exceeds state-of-the-art performance, sometimes by a large margin.", "target": ["Una nuova architettura ad alte prestazioni per end-to-end named entity recognition e relation extraction che è veloce da addestrare."]} +{"source": "In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks.Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by 80\\%.Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs.We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics.We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks.We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them.We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared.", "target": ["Schema di Bayes variazionale per reti neurali ricorrenti"]} +{"source": "Over the passage of time Unmanned Autonomous Vehicles (UAVs), especiallyAutonomous flying drones grabbed a lot of attention in Artificial Intelligence.Since electronic technology is getting smaller, cheaper and more efficient, hugeadvancement in the study of UAVs has been observed recently.From monitoringfloods, discerning the spread of algae in water bodies to detecting forest trail, theirapplication is far and wide.Our work is mainly focused on autonomous flyingdrones where we establish a case study towards efficiency, robustness and accuracyof UAVs where we showed our results well supported through experiments.We provide details of the software and hardware architecture used in the study.Wefurther discuss about our implementation algorithms and present experiments thatprovide a comparison between three different state-of-the-art algorithms namelyTrailNet, InceptionResnet and MobileNet in terms of accuracy, robustness, powerconsumption and inference time.In our study, we have shown that MobileNet hasproduced better results with very less computational requirement and power consumption.We have also reported the challenges we have faced during our workas well as a brief discussion on our future work to improve safety features andperformance.", "target": ["caso di studio sul modello ottimale di deep learning per gli UAV"]} +{"source": "Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure. The Transformer (Vaswani et al., 2017), a sequence model based on self-attention, has achieved compelling results in many generation tasks that require maintaining long-range coherence.This suggests that self-attention might also be well-suited to modeling music.In musical composition and performance, however, relative timing is critically important. Existing approaches for representing relative positional information in the Transformer modulate attention based on pairwise distance (Shaw et al., 2018). This is impractical for long sequences such as musical compositions since their memory complexity is quadratic in the sequence length. We propose an algorithm that reduces the intermediate memory requirements to linear in the sequence length.This enables us to demonstrate that a Transformer with our modified relative attention mechanism can generate minute-long (thousands of steps) compositions with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies. We evaluate the Transformer with our relative attention mechanism on two datasets, JSB Chorales and Piano-e-competition, and obtain state-of-the-art results on the latter.", "target": ["Mostriamo il primo utilizzo con successo di Transformer nella generazione di musica che mostra una struttura a lungo termine."]} +{"source": "Sequential decision problems for real-world applications often need to be solved in real-time, requiring algorithms to perform well with a restricted computational budget.Width-based lookaheads have shown state-of-the-art performance in classical planning problems as well as over the Atari games with tight budgets.In this work we investigate width-based lookaheads over Stochastic Shortest paths (SSP).We analyse why width-based algorithms perform poorly over SSP problems, and overcome these pitfalls proposing a method to estimate costs-to-go.We formalize width-based lookaheads as an instance of the rollout algorithm, give a definition of width for SSP problems and explain its sample complexity.Our experimental results over a variety of SSP benchmarks show the algorithm to outperform other state-of-the-art rollout algorithms such as UCT and RTDP.", "target": ["Proponiamo un nuovo algoritmo di Monte Carlo Tree Search / rollout che utilizza la ricerca basata sulla larghezza per costruire un lookahead."]} +{"source": "Deep Neural Networks (DNNs) are known for excellent performance in supervised tasks such as classification.Convolutional Neural Networks (CNNs), in particular, can learn effective features and build high-level representations that can be used forclassification, but also for querying and nearest neighbor search.However, CNNs have also been shown to suffer from a performance drop when the distribution of the data changes from training to test data.In this paper we analyze the internalrepresentations of CNNs and observe that the representations of unseen data in each class, spread more (with higher variance) in the embedding space of the CNN compared to representations of the training data.More importantly, this difference is more extreme if the unseen data comes from a shifted distribution.Based on this observation, we objectively evaluate the degree of representation’s variance in each class by applying eigenvalue decomposition on the within-class covariance of the internal representations of CNNs and observe the same behaviour.This can be problematic as larger variances might lead to mis-classification if the sample crosses the decision boundary of its class.We apply nearest neighbor classification on the representations and empirically show that the embeddings with the high variance actually have significantly worse KNN classification performances, although this could not be foreseen from their end-to-end classification results.To tackle this problem, we propose Deep Within-Class Covariance Analysis (DWCCA), a deep neural network layer that significantly reduces the within-class covariance of a DNN’s representation, improving performance on unseen test data from a shifted distribution.We empirically evaluate DWCCA on two datasets for Acoustic Scene Classification (DCASE2016 and DCASE2017).We demonstrate that not only does DWCCA significantly improve the network’s internal representation, italso increases the end-to-end classification accuracy, especially when the test set exhibits a slight distribution shift.By adding DWCCA to a VGG neural network, we achieve around 6 percentage points improvement in the case of a distributionmismatch.", "target": ["Proponiamo un nuovo layer di deep neural network per normalizzare la covarianza all'interno della classe di una rappresentazione interna in una rete neurale che risulta in un miglioramento significativo della generalizzazione delle rappresentazioni apprese."]} +{"source": "Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic looking images.A fundamental characteristic of generative models is their ability to produce multi-modal outputs.However, while training, they are often susceptible to mode collapse, which means that the model is limited in mapping the input noise to only a few modes of the true data distribution.In this paper, we draw inspiration from Determinantal Point Process (DPP) to devise a generative model that alleviates mode collapse while producing higher quality samples.DPP is an elegant probabilistic measure used to model negative correlations within a subset and hence quantify its diversity.We use DPP kernel to model the diversity in real data as well as in synthetic data.Then, we devise a generation penalty term that encourages the generator to synthesize data with a similar diversity to real data.In contrast to previous state-of-the-art generative models that tend to use additional trainable parameters or complex training paradigms, our method does not change the original training scheme.Embedded in an adversarial training and variational autoencoder, our Generative DPP approach shows a consistent resistance to mode-collapse on a wide-variety of synthetic data and natural image datasets including MNIST, CIFAR10, and CelebA, while outperforming state-of-the-art methods for data-efficiency, convergence-time, and generation quality.Our code will be made publicly available.", "target": ["L'aggiunta di un criterio di diversità ispirato a DPP nell'obiettivo GAN evita il mode collapse e porta a generazioni migliori."]} +{"source": "Despite existing work on ensuring generalization of neural networks in terms of scale sensitive complexity measures, such as norms, margin and sharpness, these complexity measures do not offer an explanation of why neural networks generalize better with over-parametrization.In this work we suggest a novel complexity measure based on unit-wise capacities resulting in a tighter generalization bound for two layer ReLU networks.Our capacity bound correlates with the behavior of test error with increasing network sizes (within the range reported in the experiments), and could partly explain the improvement in generalization with over-parametrization.We further present a matching lower bound for the Rademacher complexity that improves over previous capacity lower bounds for neural networks.", "target": ["Suggeriamo un bound di generalizzazione che potrebbe spiegare in parte il miglioramento della generalizzazione con la sovra-parametrizzazione."]} +{"source": "We introduce three generic point cloud processing blocks that improve both accuracy and memory consumption of multiple state-of-the-art networks, thus allowing to design deeper and more accurate networks.The novel processing blocks that facilitate efficient information flow are a convolution-type operation block for point sets that blends neighborhood information in a memory-efficient manner; a multi-resolution point cloud processing block; and a crosslink block that efficiently shares information across low- and high-resolution processing branches.Combining these blocks, we design significantly wider and deeper architectures.We extensively evaluate the proposed architectures on multiple point segmentation benchmarks (ShapeNetPart, ScanNet, PartNet) and report systematic improvements in terms of both accuracy and memory consumption by using our generic modules in conjunction with multiple recent architectures (PointNet++, DGCNN, SpiderCNN, PointCNN).We report a 9.7% increase in IoU on the PartNet dataset, which is the most complex, while decreasing memory footprint by 57%.", "target": ["Introduciamo tre blocchi generici di elaborazione delle nuvole di punti che migliorano sia la precisione che il consumo di memoria di più reti allo stato dell'arte, permettendo così di progettare reti più profonde e accurate."]} +{"source": "End-to-end acoustic-to-word speech recognition models have recently gained popularity because they are easy to train, scale well to large amounts of training data, and do not require a lexicon.In addition, word models may also be easier to integrate with downstream tasks such as spoken language understanding, because inference (search) is much simplified compared to phoneme, character or any other sort of sub-word units.In this paper, we describe methods to construct contextual acoustic word embeddings directly from a supervised sequence-to-sequence acoustic-to-word speech recognition model using the learned attention distribution.On a suite of 16 standard sentence evaluation tasks, our embeddings show competitive performance against a word2vec model trained on the speech transcriptions.In addition, we evaluate these embeddings on a spoken language understanding task and observe that our embeddings match the performance of text-based embeddings in a pipeline of first performing speech recognition and then constructing word embeddings from transcriptions.", "target": ["Metodi per apprendere word embedding acustici contestuali da un modello di riconoscimento vocale end-to-end, che si comportano in modo competitivo con i word embedding basati sul testo."]} +{"source": "Unsupervised monocular depth estimation has made great progress after deeplearning is involved.Training with binocular stereo images is considered as agood option as the data can be easily obtained.However, the depth or disparityprediction results show poor performance for the object boundaries.The mainreason is related to the handling of occlusion areas during the training.In this paper,we propose a novel method to overcome this issue.Exploiting disparity mapsproperty, we generate an occlusion mask to block the back-propagation of the occlusionareas during image warping.We also design new networks with flippedstereo images to induce the networks to learn occluded boundaries.It shows thatour method achieves clearer boundaries and better evaluation results on KITTIdriving dataset and Virtual KITTI dataset.", "target": ["Questo articolo propone un metodo di masking che risolve i precedenti risultati sfocati della stima della profondità monoculare non supervisionata causata dall'occlusione"]} +{"source": "Graph classification is currently dominated by graph kernels, which, while powerful, suffer some significant limitations.Convolutional Neural Networks (CNNs) offer a very appealing alternative.However, processing graphs with CNNs is not trivial.To address this challenge, many sophisticated extensions of CNNs have recently been proposed.In this paper, we reverse the problem: rather than proposing yet another graph CNN model, we introduce a novel way to represent graphs as multi-channel image-like structures that allows them to be handled by vanilla 2D CNNs.Despite its simplicity, our method proves very competitive to state-of-the-art graph kernels and graph CNNs, and outperforms them by a wide margin on some datasets.It is also preferable to graph kernels in terms of time complexity.Code and data are publicly available.", "target": ["Introduciamo un nuovo modo di rappresentare i grafi come strutture multicanale simili alle immagini che permette loro di essere gestiti da CNN 2D standard."]} +{"source": "The key attribute that drives the unprecedented success of modern Recurrent Neural Networks (RNNs) on learning tasks which involve sequential data, is their ever-improving ability to model intricate long-term temporal dependencies.However, a well established measure of RNNs' long-term memory capacity is lacking, and thus formal understanding of their ability to correlate data throughout time is limited.Though depth efficiency in convolutional networks is well established by now, it does not suffice in order to account for the success of deep RNNs on inputs of varying lengths, and the need to address their 'time-series expressive power' arises.In this paper, we analyze the effect of depth on the ability of recurrent networks to express correlations ranging over long time-scales.To meet the above need, we introduce a measure of the information flow across time that can be supported by the network, referred to as the Start-End separation rank.Essentially, this measure reflects the distance of the function realized by the recurrent network from a function that models no interaction whatsoever between the beginning and end of the input sequence.We prove that deep recurrent networks support Start-End separation ranks which are exponentially higher than those supported by their shallow counterparts.Moreover, we show that the ability of deep recurrent networks to correlate different parts of the input sequence increases exponentially as the input sequence extends, while that of vanilla shallow recurrent networks does not adapt to the sequence length at all.Thus, we establish that depth brings forth an overwhelming advantage in the ability of recurrent networks to model long-term dependencies, and provide an exemplar of quantifying this key attribute which may be readily extended to other RNN architectures of interest, e.g. variants of LSTM networks.We obtain our results by considering a class of recurrent networks referred to as Recurrent Arithmetic Circuits (RACs), which merge the hidden state with the input via the Multiplicative Integration operation.", "target": ["Proponiamo una misura della memoria a lungo termine e dimostriamo che le deep recurrent network sono molto più adatte a modellare le dipendenze temporali a lungo termine rispetto a quelle superficiali."]} +{"source": "Holistically exploring the perceptual and neural representations underlying animal communication has traditionally been very difficult because of the complexity of the underlying signal.We present here a novel set of techniques to project entire communicative repertoires into low dimensional spaces that can be systematically sampled from, exploring the relationship between perceptual representations, neural representations, and the latent representational spaces learned by machine learning algorithms.We showcase this method in one ongoing experiment studying sequential and temporal maintenance of context in songbird neural and perceptual representations of syllables.We further discuss how studying the neural mechanisms underlying the maintenance of the long-range information content present in birdsong can inform and be informed by machine sequence modeling.", "target": ["Confrontiamo le rappresentazioni percettive, neurali e modellate della comunicazione animale utilizzando il machine learning, il comportamento e la fisiologia."]} +{"source": "The information bottleneck principle (Shwartz-Ziv & Tishby, 2017) suggests that SGD-based training of deep neural networks results in optimally compressed hidden layers, from an information theoretic perspective.However, this claim was established on toy data.The goal of the work we present here is to test these claims in a realistic setting using a larger and deeper convolutional architecture, a ResNet model.We trained PixelCNN++ models as inverse representation decoders to measure the mutual information between hidden layers of a ResNet and input image data, when trained for (1) classification and (2) autoencoding.We find that two stages of learning happen for both training regimes, and that compression does occur, even for an autoencoder.Sampling images by conditioning on hidden layers’ activations offers an intuitive visualisation to understand what a ResNets learns to forget.", "target": ["Il principio dell'Information Bottleneck applicato alle ResNets, usando i modelli PixelCNN++ per decodificare l'informazione mutua e generare condizionatamente immagini per l'illustrazione delle informazioni"]} +{"source": "We study the problem of safe adaptation: given a model trained on a variety of past experiences for some task, can this model learn to perform that task in a new situation while avoiding catastrophic failure?This problem setting occurs frequently in real-world reinforcement learning scenarios such as a vehicle adapting to drive in a new city, or a robotic drone adapting a policy trained only in simulation.While learning without catastrophic failures is exceptionally difficult, prior experience can allow us to learn models that make this much easier.These models might not directly transfer to new settings, but can enable cautious adaptation that is substantially safer than na\\\"{i}ve adaptation as well as learning from scratch.Building on this intuition, we propose risk-averse domain adaptation (RADA).RADA works in two steps: it first trains probabilistic model-based RL agents in a population of source domains to gain experience and capture epistemic uncertainty about the environment dynamics.Then, when dropped into a new environment, it employs a pessimistic exploration policy, selecting actions that have the best worst-case performance as forecasted by the probabilistic model.We show that this simple maximin policy accelerates domain adaptation in a safety-critical driving environment with varying vehicle sizes.We compare our approach against other approaches for adapting to new environments, including meta-reinforcement learning.", "target": ["L'adattamento di un agente RL in un ambiente di destinazione con dinamiche sconosciute è veloce e sicuro quando trasferiamo l'esperienza precedente in una varietà di ambienti e poi selezioniamo azioni avverse al rischio durante l'adattamento."]} +{"source": "We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers.Our model builds an object-based scene representation and translates sentences into executable, symbolic programs.To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation.Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to.Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences.We use curriculum learning to guide the searching over the large compositional space of images and language.Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences.Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains.It also empowers applications including visual question answering and bidirectional image-text retrieval.", "target": ["Proponiamo il Neuro-Symbolic Concept Learner (NS-CL), un modello che impara concetti visivi, parole e parsing semantico di frasi senza supervisione esplicita su nessuno di essi."]} +{"source": "Bayesian inference offers a theoretically grounded and general way to train neural networks and can potentially give calibrated uncertainty.However, it is challenging to specify a meaningful and tractable prior over the network parameters, and deal with the weight correlations in the posterior.To this end, this paper introduces two innovations:(i) a Gaussian process-based hierarchical model for the network parameters based on recently introduced unit embeddings that can flexibly encode weight structures, and(ii) input-dependent contextual variables for the weight prior that can provide convenient ways to regularize the function space being modeled by the network through the use of kernels. We show these models provide desirable test-time uncertainty estimates, demonstrate cases of modeling inductive biases for neural networks with kernels and demonstrate competitive predictive performance on an active learning benchmark.", "target": ["Introduciamo un Gaussian Process Prior sui pesi in una rete neurale ed esploriamo la sua capacità di modellare i pesi dipendenti dall'input con benefici per vari task, inclusa la stima dell'incertezza e la generalizzazione nei setting low-sample."]} +{"source": "We perform an in-depth investigation of the suitability of self-attention models for character-level neural machine translation.We test the standard transformer model, as well as a novel variant in which the encoder block combines information from nearby characters using convolution.We perform extensive experiments on WMT and UN datasets, testing both bilingual and multilingual translation to English using up to three input languages (French, Spanish, and Chinese).Our transformer variant consistently outperforms the standard transformer at the character-level and converges faster while learning more robust character-level alignments.", "target": ["Eseguiamo un'indagine approfondita sull'idoneità dei modelli di self-attention per character-level neural machine translation."]} +{"source": "The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures.Many tasks in radiology, for example, are largely problems of multi-label classification wherein medical images are interpreted to indicate multiple present or suspected pathologies.Clinical settings drive the necessity for high accuracy simultaneously across a multitude of pathological outcomes and greatly limit the utility of tools which consider only a subset.This issue is exacerbated by a general scarcity of training data and maximizes the need to extract clinically relevant features from available samples -- ideally without the use of pre-trained models which may carry forward undesirable biases from tangentially related tasks.We present and evaluate a partial solution to these constraints in using LSTMs to leverage interdependencies among target labels in predicting 14 pathologic patterns from chest x-rays and establish state of the art results on the largest publicly available chest x-ray dataset from the NIH without pre-training.Furthermore, we propose and discuss alternative evaluation metrics and their relevance in clinical practice.", "target": ["presentiamo i risultati allo stato dell'arte dell'uso delle reti neurali per diagnosticare le radiografie del torace"]} +{"source": "Semmelhack et al. (2014) have achieved high classification accuracy in distinguishing swim bouts of zebrafish using a Support Vector Machine (SVM).Convolutional Neural Networks (CNNs) have reached superior performance in various image recognition tasks over SVMs, but these powerful networks remain a black box.Reaching better transparency helps to build trust in their classifications and makes learned features interpretable to experts.Using a recently developed technique called Deep Taylor Decomposition, we generated heatmaps to highlight input regions of high relevance for predictions.We find that our CNN makes predictions by analyzing the steadiness of the tail's trunk, which markedly differs from the manually extracted features used by Semmelhack et al. (2014).We further uncovered that the network paid attention to experimental artifacts.Removing these artifacts ensured the validity of predictions.After correction, our best CNN beats the SVM by 6.12%, achieving a classification accuracy of 96.32%.Our work thus demonstrates the utility of AI explainability for CNNs.", "target": ["Dimostriamo l'utilità di una recente tecnica di AI explainability visualizzando le feature apprese da una CNN addestrata sulla classificazione binaria dei movimenti di zebrafish."]} +{"source": "When communicating, humans rely on internally-consistent language representations.That is, as speakers, we expect listeners to behave the same way we do when we listen.This work proposes several methods for encouraging such internal consistency in dialog agents in an emergent communication setting.We consider two hypotheses about the effect of internal-consistency constraints:1) that they improve agents’ ability to refer to unseen referents, and2) that they improve agents’ ability to generalize across communicative roles (e.g. performing as a speaker de- spite only being trained as a listener).While we do not find evidence in favor of the former, our results show significant support for the latter.", "target": ["I vincoli di coerenza interna migliorano la capacità degli agenti di sviluppare protocolli emergenti che generalizzano attraverso i ruoli comunicativi."]} +{"source": "Neural networks (NNs) are able to perform tasks that rely on compositional structure even though they lack obvious mechanisms for representing this structure.To analyze the internal representations that enable such success, we propose ROLE, a technique that detects whether these representations implicitly encode symbolic structure.ROLE learns to approximate the representations of a target encoder E by learning a symbolic constituent structure and an embedding of that structure into E’s representational vector space.The constituents of the approximating symbol structure are defined by structural positions — roles — that can be filled by symbols.We show that when E is constructed to explicitly embed a particular type of structure (e.g., string or tree), ROLE successfully extracts the ground-truth roles defining that structure.We then analyze a seq2seq network trained to perform a more complex compositional task (SCAN), where there is no ground truth role scheme available.For this model, ROLE successfully discovers an interpretable symbolic structure that the model implicitly uses to perform the SCAN task, providing a comprehensive account of the link between the representations and the behavior of a notoriously hard-to-interpret type of model.We verify the causal importance of the discovered symbolic structure by showing that, when we systematically manipulate hidden embeddings based on this symbolic structure, the model’s output is also changed in the way predicted by our analysis.Finally, we use ROLE to explore whether popular sentence embedding models are capturing compositional structure and find evidence that they are not; we conclude by discussing how insights from ROLE can be used to impart new inductive biases that will improve the compositional abilities of such models.", "target": ["Introduciamo una nuova tecnica di analisi che scopre la struttura compositiva ed interpretabile nelle reti neurali ricorrenti notoriamente difficili da interpretare."]} +{"source": "The vertebrate visual system is hierarchically organized to process visual information in successive stages.Neural representations vary drastically across the first stages of visual processing: at the output of the retina, ganglion cell receptive fields (RFs) exhibit a clear antagonistic center-surround structure, whereas in the primary visual cortex (V1), typical RFs are sharply tuned to a precise orientation.There is currently no unified theory explaining these differences in representations across layers.Here, using a deep convolutional neural network trained on image recognition as a model of the visual system, we show that such differences in representation can emerge as a direct consequence of different neural resource constraints on the retinal and cortical networks, and for the first time we find a single model from which both geometries spontaneously emerge at the appropriate stages of visual processing.The key constraint is a reduced number of neurons at the retinal output, consistent with the anatomy of the optic nerve as a stringent bottleneck.Second, we find that, for simple downstream cortical networks, visual representations at the retinal output emerge as nonlinear and lossy feature detectors, whereas they emerge as linear and faithful encoders of the visual scene for more complex cortical networks.This result predicts that the retinas of small vertebrates (e.g. salamander, frog) should perform sophisticated nonlinear computations, extracting features directly relevant to behavior, whereas retinas of large animals such as primates should mostly encode the visual scene linearly and respond to a much broader range of stimuli.These predictions could reconcile the two seemingly incompatible views of the retina as either performing feature extraction or efficient coding of natural scenes, by suggesting that all vertebrates lie on a spectrum between these two objectives, depending on the degree of neural resources allocated to their visual system.", "target": ["Abbiamo riprodotto le rappresentazioni neurali trovate nei sistemi visivi biologici simulando i loro vincoli di risorse neurali in un deep convolutional model."]} +{"source": "While it has not yet been proven, empirical evidence suggests that model generalization is related to local properties of the optima which can be described via the Hessian.We connect model generalization with the local property of a solution under the PAC-Bayes paradigm.In particular, we prove that model generalization ability is related to the Hessian, the higher-order \"smoothness\" terms characterized by the Lipschitz constant of the Hessian, and the scales of the parameters.Guided by the proof, we propose a metric to score the generalization capability of the model, as well as an algorithm that optimizes the perturbed model accordingly.", "target": ["una teoria che collega l'Hessiano della soluzione e il potere di generalizzazione del modello"]} +{"source": "Unsupervised learning is about capturing dependencies between variables and is driven by the contrast between the probable vs improbable configurations of these variables, often either via a generative model which only samples probable ones or with an energy function (unnormalized log-density) which is low for probable ones and high for improbable ones.Here we consider learning both an energy function and an efficient approximate sampling mechanism for the corresponding distribution.Whereas the critic (or discriminator) in generative adversarial networks (GANs) learns to separate data and generator samples, introducing an entropy maximization regularizer on the generator can turn the interpretation of the critic into an energy function, which separates the training distribution from everything else, and thus can be used for tasks like anomaly or novelty detection. This paper is motivated by the older idea of sampling in latent space rather than data space because running a Monte-Carlo Markov Chain (MCMC) in latent space has been found to be easier and more efficient, and because a GAN-like generator can convert latent space samples to data space samples.For this purpose, we show how a Markov chain can be run in latent space whose samples can be mapped to data space, producing better samples.These samples are also used for the negative phase gradient required to estimate the log-likelihood gradient of the data space energy function.To maximize entropy at the output of the generator, we take advantage of recently introduced neural estimators of mutual information.We find that in addition to producing a useful scoring function for anomaly detection, the resulting approach produces sharp samples (like GANs) while covering the modes well, leading to high Inception and Fréchet scores.", "target": ["Abbiamo introdotto la massimizzazione dell'entropia nelle GAN, portando a una reinterpretazione del critic come una funzione energetica."]} +{"source": "Neural Style Transfer has become a popular technique forgenerating images of distinct artistic styles using convolutional neural networks.Thisrecent success in image style transfer has raised the question ofwhether similar methods can be leveraged to alter the “style” of musicalaudio.In this work, we attempt long time-scale high-quality audio transferand texture synthesis in the time-domain that captures harmonic,rhythmic, and timbral elements related to musical style, using examples thatmay have different lengths and musical keys.We demonstrate the abilityto use randomly initialized convolutional neural networks to transferthese aspects of musical style from one piece onto another using 3different representations of audio: the log-magnitude of the Short TimeFourier Transform (STFT), the Mel spectrogram, and the Constant-Q Transformspectrogram.We propose using these representations as a way ofgenerating and modifying perceptually significant characteristics ofmusical audio content.We demonstrate each representation'sshortcomings and advantages over others by carefully designingneural network structures that complement the nature of musical audio.Finally, we show that the mostcompelling “style” transfer examples make use of an ensemble of theserepresentations to help capture the varying desired characteristics ofaudio signals.", "target": ["Presentiamo un algoritmo di transfer di stile audio musicale su lunga scala temporale che sintetizza l'audio nel dominio del tempo, ma utilizza rappresentazioni tempo-frequenza dell'audio."]} +{"source": "To communicate with new partners in new contexts, humans rapidly form new linguistic conventions.Recent language models trained with deep neural networks are able to comprehend and produce the existing conventions present in their training data, but are not able to flexibly and interactively adapt those conventions on the fly as humans do.We introduce a repeated reference task as a benchmark for models of adaptation in communication and propose a regularized continual learning framework that allows an artificial agent initialized with a generic language model to more accurately and efficiently understand their partner over time.We evaluate this framework through simulations on COCO and in real-time reference game experiments with human partners.", "target": ["Proponiamo un benchmark task di repeated reference e un approccio di continual learning regolarizzato per la comunicazione adattiva con gli esseri umani in domini non familiari"]} +{"source": "Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem.We introduce a pooling method for sets of feature vectors based on sorting features across elements of the set.This can be used to construct a permutation-equivariant auto-encoder that avoids this responsibility problem.On a toy dataset of polygons and a set version of MNIST, we show that such an auto-encoder produces considerably better reconstructions and representations.Replacing the pooling function in existing set encoders with FSPool improves accuracy and convergence speed on a variety of datasets.", "target": ["Sorting nell'encoder e annullamento del sorting nel decoder per evitare il problema della responsabilità nei set auto-encoder"]} +{"source": "We present a method for policy learning to navigate indoor environments.We adopt a hierarchical policy approach, where two agents are trained to work in cohesion with one another to perform a complex navigation task.A Planner agent operates at a higher level and proposes sub-goals for an Executor agent.The Executor reports an embedding summary back to the Planner as additional side information at the end of its series of operations for the Planner's next sub-goal proposal.The end goal is generated by the environment and exposed to the Planner which then decides which set of sub-goals to propose to the Executor.We show that this Planner-Executor setup drastically increases the sample efficiency of our method over traditional single agent approaches, effectively mitigating the difficulty accompanying long series of actions with a sparse reward signal.On the challenging Habitat environment which requires navigating various realistic indoor environments, we demonstrate that our approach offers a significant improvement over prior work for navigation.", "target": ["Presentiamo un framework di hierarchical learning per la navigazione in un ambiente di embodied learning"]} +{"source": "Saliency methods aim to explain the predictions of deep neural networks.These methods lack reliability when the explanation is sensitive to factors that do not contribute to the model prediction.We use a simple and common pre-processing step ---adding a mean shift to the input data--- to show that a transformation with no effect on the model can cause numerous methods to incorrectly attribute.We define input invariance as the requirement that a saliency method mirror the sensitivity of the model with respect to transformations of the input.We show, through several examples, that saliency methods that do not satisfy a input invariance property are unreliable and can lead to misleading and inaccurate attribution.", "target": ["L'attribuzione a volte può essere fuorviante"]} +{"source": "Large Transformer models routinely achieve state-of-the-art results ona number of tasks but training these models can be prohibitively costly,especially on long sequences.We introduce two techniques to improvethe efficiency of Transformers.For one, we replace dot-product attentionby one that uses locality-sensitive hashing, changing its complexityfrom O(L^2) to O(L), where L is the length of the sequence.Furthermore, we use reversible residual layers instead of the standardresiduals, which allows storing activations only once in the trainingprocess instead of N times, where N is the number of layers.The resulting model, the Reformer, performs on par with Transformer modelswhile being much more memory-efficient and much faster on long sequences.", "target": ["Trasformer efficiente con locality-sensitive hashing e reversible layers"]} +{"source": "Obtaining policies that can generalise to new environments in reinforcement learning is challenging.In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments.We propose a grounded policy learning problem, Read to Fight Monsters (RTFM), in which the agent must jointly reason over a language goal, relevant dynamics described in a document, and environment observations.We procedurally generate environment dynamics and corresponding language descriptions of the dynamics, such that agents must read to understand new environment dynamics instead of memorising any particular information.In addition, we propose txt2π, a model that captures three-way interactions between the goal, document, and observations.On RTFM, txt2π generalises to new environments with dynamics not seen during training via reading.Furthermore, our model outperforms baselines such as FiLM and language-conditioned CNNs on RTFM.Through curriculum learning, txt2π produces policies that excel on complex RTFM tasks requiring several reasoning and coreference steps.", "target": ["Mostriamo che la comprensione del linguaggio attraverso la lettura è un modo promettente per imparare policy che generalizzano a nuovi ambienti."]} +{"source": "An open question in the Deep Learning community is why neural networks trained with Gradient Descent generalize well on real datasets even though they are capable of fitting random data.We propose an approach to answering this question based on a hypothesis about the dynamics of gradient descent that we call Coherent Gradients: Gradients from similar examples are similar and so the overall gradient is stronger in certain directions where these reinforce each other.Thus changes to the network parameters during training are biased towards those that (locally) simultaneously benefit many examples when such similarity exists.We support this hypothesis with heuristic arguments and perturbative experiments and outline how this can explain several common empirical observations about Deep Learning.Furthermore, our analysis is not just descriptive, but prescriptive.It suggests a natural modification to gradient descent that can greatly reduce overfitting.", "target": ["Proponiamo un'ipotesi sul perché la gradient descent generalizza basandosi su come i gradienti per-sample interagiscono tra loro."]} +{"source": "Recent advances in deep learning have shown promising results in many low-level vision tasks.However, solving the single-image-based view synthesis is still an open problem.In particular, the generation of new images at parallel camera views given a single input image is of great interest, as it enables 3D visualization of the 2D input scenery.We propose a novel network architecture to perform stereoscopic view synthesis at arbitrary camera positions along the X-axis, or Deep 3D Pan, with \"t-shaped\" adaptive kernels equipped with globally and locally adaptive dilations.Our proposed network architecture, the monster-net, is devised with a novel t-shaped adaptive kernel with globally and locally adaptive dilation, which can efficiently incorporate global camera shift into and handle local 3D geometries of the target image's pixels for the synthesis of naturally looking 3D panned views when a 2-D input image is given.Extensive experiments were performed on the KITTI, CityScapes and our VXXLXX_STEREO indoors dataset to prove the efficacy of our method.Our monster-net significantly outperforms the state-of-the-art method, SOTA, by a large margin in all metrics of RMSE, PSNR, and SSIM.Our proposed monster-net is capable of reconstructing more reliable image structures in synthesized images with coherent geometry.Moreover, the disparity information that can be extracted from the \"t-shaped\" kernel is much more reliable than that of the SOTA for the unsupervised monocular depth estimation task, confirming the effectiveness of our method.", "target": ["Nuova architettura per la sintesi della vista stereoscopica a spostamenti arbitrari della telecamera utilizzando kernel adattivi t-shaped con dilatazioni adattive."]} +{"source": "Deep Neutral Networks(DNNs) require huge GPU memory when training on modern image/video databases.Unfortunately, the GPU memory as a hardware resource is always finite, which limits the image resolution, batch size, and learning rate that could be used for better DNN performance.In this paper, we propose a novel training approach, called Re-forwarding, that substantially reduces memory usage in training.Our approach automatically finds a subset of vertices in a DNN computation graph, and stores tensors only at these vertices during the first forward.During backward, extra local forwards (called the Re-forwarding process) are conducted to compute the missing tensors between the subset of vertices.The total memory cost becomes the sum of (1) the memory cost at the subset of vertices and (2) the maximum memory cost among local re-forwards.Re-forwarding trades training time overheads for memory and does not compromise any performance in testing.We propose theories and algorithms that achieve the optimal memory solutions for DNNs with either linear or arbitrary computation graphs.Experiments show that Re-forwarding cuts down up-to 80% of training memory on popular DNNs such as Alexnet, VGG, ResNet, Densenet and Inception net.", "target": ["Questo articolo propone la teoria fondamentale e gli algoritmi ottimali per il training di DNN, che riducono fino all'80% la memoria di training per le DNN popolari."]} +{"source": "Compression is a key step to deploy large neural networks on resource-constrained platforms.As a popular compression technique, quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight.In this paper, we study the representation power of quantized neural networks.First, we prove the universal approximability of quantized ReLU networks on a wide class of functions.Then we provide upper bounds on the number of weights and the memory size for a given approximation error bound and the bit-width of weights for function-independent and function-dependent structures.Our results reveal that, to attain an approximation error bound of $\\epsilon$, the number of weights needed by a quantized network is no more than $\\mathcal{O}\\left(\\log^5(1/\\epsilon)\\right)$ times that of an unquantized network.This overhead is of much lower order than the lower bound of the number of weights needed for the error bound, supporting the empirical success of various quantization techniques.To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.", "target": ["Questo articolo dimostra l'approssimabilità universale delle reti neurali quantizzate ReLU e presenta il bound di complessità dato un errore arbitrario."]} +{"source": "Reinforcement learning (RL) with value-based methods (e.g., Q-learning) has shown success in a variety of domains such asgames and recommender systems (RSs).When the action space is finite, these algorithms implicitly finds a policy by learning the optimal value function, which are often very efficient. However, one major challenge of extending Q-learning to tackle continuous-action RL problems is that obtaining optimal Bellman backup requires solving a continuous action-maximization (max-Q) problem.While it is common to restrict the parameterization of the Q-function to be concave in actions to simplify the max-Q problem, such a restriction might lead to performance degradation.Alternatively, when the Q-function is parameterized with a generic feed-forward neural network (NN), the max-Q problem can be NP-hard.In this work, we propose the CAQL method which minimizes the Bellman residual using Q-learning with one of several plug-and-play action optimizers.In particular, leveraging the strides of optimization theories in deep NN, we show that max-Q problem can be solved optimally with mixed-integer programming (MIP)---when the Q-function has sufficient representation power, this MIP-based optimization induces better policies and is more robust than counterparts, e.g., CEM or GA, that approximate the max-Q solution.To speed up training of CAQL, we develop three techniques, namely(i) dynamic tolerance,(ii) dual filtering, and(iii) clustering.To speed up inference of CAQL, we introduce the action function that concurrently learns the optimal policy.To demonstrate the efficiency of CAQL we compare it with state-of-the-art RL algorithms on benchmark continuous control problems that have different degrees of action constraints and show that CAQL significantly outperforms policy-based methods in heavily constrained environments.", "target": ["Un framework generale di reinforcement learning basato sul valore per il controllo continuo"]} +{"source": "Generative Adversarial Networks (GANs) have proven to be a powerful framework for learning to draw samples from complex distributions.However, GANs are also notoriously difficult to train, with mode collapse and oscillations a common problem.We hypothesize that this is at least in part due to the evolution of the generator distribution and the catastrophic forgetting tendency of neural networks, which leads to the discriminator losing the ability to remember synthesized samples from previous instantiations of the generator.Recognizing this, our contributions are twofold.First, we show that GAN training makes for a more interesting and realistic benchmark for continual learning methods evaluation than some of the more canonical datasets.Second, we propose leveraging continual learning techniques to augment the discriminator, preserving its ability to recognize previous generator samples.We show that the resulting methods add only a light amount of computation, involve minimal changes to the model, and result in better overall performance on the examined image and text generation tasks.", "target": ["Il training generativo delle reti adversariali è un problema di continual learning."]} +{"source": "Many problems with large-scale labeled training data have been impressively solved by deep learning.However, Unseen Class Categorization (UCC) with minimal information provided about target classes is the most commonly encountered setting in industry, which remains a challenging research problem in machine learning.Previous approaches to UCC either fail to generate a powerful discriminative feature extractor or fail to learn a flexible classifier that can be easily adapted to unseen classes.In this paper, we propose to address these issues through network reparameterization, \\textit{i.e.}, reparametrizing the learnable weights of a network as a function of other variables, by which we decouple the feature extraction part and the classification part of a deep classification model to suit the special setting of UCC, securing both strong discriminability and excellent adaptability.Extensive experiments for UCC on several widely-used benchmark datasets in the settings of zero-shot and few-shot learning demonstrate that, our method with network reparameterization achieves state-of-the-art performance.", "target": ["Un quadro unificato per l'apprendimento few shot e l'apprendimento zero-shot basato sulla riparametrizzazione della rete"]} +{"source": "Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure.Experimental identification of a protein's structure can be time-consuming, prohibitively expensive, and not always possible. Alternatively, protein folding can be modeled using computational methods, which however are not guaranteed to always produce optimal results.GraphQA is a graph-based method to estimate the quality of protein models, that possesses favorable properties such as representation learning, explicit modeling of both sequential and 3D structure, geometric invariance and computational efficiency. In this work, we demonstrate significant improvements of the state-of-the-art for both hand-engineered and representation-learning approaches, as well as carefully evaluating the individual contributions of GraphQA.", "target": ["GraphQA è un metodo basato sui grafi per la valutazione della qualità delle proteine che migliora lo stato dell'arte sia per gli approcci hand-engineered sia per quelli di representation-learning"]} +{"source": "We study the problem of training machine learning models incrementally using active learning with access to imperfect or noisy oracles.We specifically consider the setting of batch active learning, in which multiple samples are selected as opposed to a single sample as in classical settings so as to reduce the training overhead.Our approach bridges between uniform randomness and score based importance sampling of clusters when selecting a batch of new samples.Experiments onbenchmark image classification datasets (MNIST, SVHN, and CIFAR10) shows improvement over existing active learning strategies.We introduce an extra denoising layer to deep networks to make active learning robust to label noises and show significant improvements.", "target": ["Affrontiamo active learning nel setting batch con oracoli rumorosi e usiamo l'incertezza del modello per codificare la qualità della decisione dell'algoritmo di active learning durante l'acquisizione."]} +{"source": "Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another.Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn't have control over the synthesized output.Moreover, the style transfer depends on the hyper-parameters of the model with varying ``optimum\" for different input images.Therefore, if the stylized output is not appealing to the user, she/he has to try multiple models or retrain one with different hyper-parameters to get a favorite stylization.In this paper, we address these issues by proposing a novel method which allows adjustment of crucial hyper-parameters, after the training and in real-time, through a set of manually adjustable parameters.These parameters enable the user to modify the synthesized outputs from the same pair of style/content images, in search of a favorite stylized image.Our quantitative and qualitative experiments indicate how adjusting these parameters is comparable to retraining the model with different hyper-parameters.We also demonstrate how these parameters can be randomized to generate results which are diverse but still very similar in style and content.", "target": ["Stochastic style transfer con feature regolabili."]} +{"source": "Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards.However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales.To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples. As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data.This framework effectively separates the representation of what instructions require from how they can be executed.In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements.We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.", "target": ["Proponiamo AGILE, un framework per il training degli agenti per eseguire istruzioni a partire da esempi dei rispettivi goal-state."]} +{"source": "We present Multitask Soft Option Learning (MSOL), a hierarchical multi-task framework based on Planning-as-Inference.MSOL extends the concept of Options, using separate variational posteriors for each task, regularized by a shared prior.The learned soft-options are temporally extended, allowing a higher-level master policy to train faster on new tasks by making decisions with lower frequency.Additionally, MSOL allows fine-tuning of soft-options for new tasks without unlearning previously useful behavior, and avoids problems with local minima in multitask training.We demonstrate empirically that MSOL significantly outperforms both hierarchical and flat transfer-learning baselines in challenging multi-task environments.", "target": ["In Hierarchical RL, introduciamo la nozione di opzione 'soft', cioè adattabile, e mostriamo che questo aiuta l'apprendimento in ambienti multitask."]} +{"source": "We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning.The learning objective is achieved by providing signal to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE.We design an unsupervised and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE.In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables.Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning.On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed.", "target": ["Apprendimento di un modello generativo controllabile mediante il disentanglement learning di latent representation."]} +{"source": "Neural language models (NLMs) are generative, and they model the distribution of grammatical sentences.Trained on huge corpus, NLMs are pushing the limit of modeling accuracy.Besides, they have also been applied to supervised learning tasks that decode text, e.g., automatic speech recognition (ASR).By re-scoring the n-best list, NLM can select grammatically more correct candidate among the list, and significantly reduce word/char error rate.However, the generative nature of NLM may not guarantee a discrimination between “good” and “bad” (in a task-specific sense) sentences, resulting in suboptimal performance.This work proposes an approach to adapt a generative NLM to a discriminative one.Different from the commonly used maximum likelihood objective, the proposed method aims at enlarging the margin between the “good” and “bad” sentences.It is trained end-to-end and can be widely applied to tasks that involve the re-scoring of the decoded text.Significant gains are observed in both ASR and statistical machine translation (SMT) tasks.", "target": ["Migliorare il language model per i task di apprendimento supervised"]} +{"source": "Conventionally, convolutional neural networks (CNNs) process different images with the same set of filters.However, the variations in images pose a challenge to this fashion.In this paper, we propose to generate sample-specific filters for convolutional layers in the forward pass.Since the filters are generated on-the-fly, the model becomes more flexible and can better fit the training data compared to traditional CNNs.In order to obtain sample-specific features, we extract the intermediate feature maps from an autoencoder.As filters are usually high dimensional, we propose to learn a set of coefficients instead of a set of filters.These coefficients are used to linearly combine the base filters from a filter repository to generate the final filters for a CNN.The proposed method is evaluated on MNIST, MTFL and CIFAR10 datasets.Experiment results demonstrate that the classification accuracy of the baseline model can be improved by using the proposed filter generation method.", "target": ["generare dinamicamente filtri condizionati sull'immagine di ingresso per le CNN in ogni forward pass"]} +{"source": "We propose a new anytime neural network which allows partial evaluation by subnetworks with different widths as well as depths.Compared to conventional anytime networks only with the depth controllability, the increased architectural diversity leads to higher resource utilization and consequent performance improvement under various and dynamic resource budgets.We highlight architectural features to make our scheme feasible as well as efficient, and show its effectiveness in image classification tasks.", "target": ["Proponiamo una nuova rete neurale anytime che permette una valutazione parziale tramite sottoreti con diverse larghezze e profondità."]} +{"source": "We propose a new model for making generalizable and diverse retrosynthetic reaction predictions.Given a target compound, the task is to predict the likely chemical reactants to produce the target.This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules.Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem.Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the model to produce a diverse set of alternative predictions.On the 50k subset of reaction examples from the United States patent literature (USPTO-50k) benchmark dataset, our model greatly improves performance over the baseline, while also generating predictions that are more diverse.", "target": ["Proponiamo un nuovo modello per fare previsioni di reazioni retrosintetiche generalizzabili e diverse."]} +{"source": "Unsupervised learning of disentangled representations is an open problem in machine learning.The Disentanglement-PyTorch library is developed to facilitate research, implementation, and testing of new variational algorithms.In this modular library, neural architectures, dimensionality of the latent space, and the training algorithms are fully decoupled, allowing for independent and consistent experiments across variational methods.The library handles the training scheduling, logging, and visualizations of reconstructions and latent space traversals.It also evaluates the encodings based on various disentanglement metrics.The library, so far, includes implementations of the following unsupervised algorithms VAE, Beta-VAE, Factor-VAE, DIP-I-VAE, DIP-II-VAE, Info-VAE, and Beta-TCVAE, as well as conditional approaches such as CVAE and IFCVAE.The library is compatible with the Disentanglement Challenge of NeurIPS 2019, hosted on AICrowd and was used to compete in the first and second stages of the challenge, where it was ranked among the best few participants.", "target": ["Disentanglement-PyTorch è una libreria per il representation learning variazionale"]} +{"source": "Trust region methods, such as TRPO, are often used to stabilize policy optimization algorithms in reinforcement learning (RL).While current trust region strategies are effective for continuous control, they typically require a large amount of on-policy interaction with the environment.To address this problem, we propose an off-policy trust region method, Trust-PCL, which exploits an observation that the optimal policy and state values of a maximum reward objective with a relative-entropy regularizer satisfy a set of multi-step pathwise consistencies along any path.The introduction of relative entropy regularization allows Trust-PCL to maintain optimization stability while exploiting off-policy data to improve sample efficiency.When evaluated on a number of continuous control tasks, Trust-PCL significantly improves the solution quality and sample efficiency of TRPO.", "target": ["Estendiamo le recenti intuizioni relative alla softmax consistency per ottenere risultati allo stato dell'arte nel controllo continuo."]} +{"source": "Deep reinforcement learning has achieved many recent successes, but our understanding of its strengths and limitations is hampered by the lack of rich environments in which we can fully characterize optimal behavior, and correspondingly diagnose individual actions against such a characterization. Here we consider a family of combinatorial games, arising from work of Erdos, Selfridge, and Spencer, and we propose their use as environments for evaluating and comparing different approaches to reinforcement learning.These games have a number of appealing features: they are challenging for current learning approaches, but they form(i) a low-dimensional, simply parametrized environment where(ii) there is a linear closed form solution for optimal behavior from any state, and(iii) the difficulty of the game can be tuned by changing environment parameters in an interpretable way.We use these Erdos-Selfridge-Spencer games not only to compare different algorithms, but also to compare approaches based on supervised and reinforcement learning, to analyze the power of multi-agent approaches in improving performance, and to evaluate generalization to environments outside the training set.", "target": ["Adattiamo una famiglia di giochi combinatori con difficoltà regolabile e una policy ottimale esprimibile come rete lineare, sviluppandola come un ricco ambiente per il reinforcement learning, mostrando contrasti nelle prestazioni con l'apprendimento supervised, e analizzando l'apprendimento e la generalizzazione multiagente."]} +{"source": "Adoption of deep learning in safety-critical systems raise the need for understanding what deep neural networks do not understand.Several methodologies to estimate model uncertainty have been proposed, but these methodologies constrain either how the neural network is trained or constructed.We present Outlier Detection In Neural networks (ODIN), an assumption-free method for detecting outlier observations during prediction, based on principles widely used in manufacturing process monitoring.By using a linear approximation of the hidden layer manifold, we add prediction-time outlier detection to models after training without altering architecture or training.We demonstrate that ODIN efficiently detect outliers during prediction on Fashion-MNIST, ImageNet-synsets and speech command recognition.", "target": ["Un metodo aggiuntivo per il deep learning per rilevare gli outlier durante la predizione"]} +{"source": "This work introduces a simple network for producing character aware word embeddings.Position agnostic and position aware character embeddings are combined to produce an embedding vector for each word.The learned word representations are shown to be very sparse and facilitate improved results on language modeling tasks, despite using markedly fewer parameters, and without the need to apply dropout.A final experiment suggests that weight sharing contributes to sparsity, increases performance, and prevents overfitting.", "target": ["Un'architettura fully connected è usata per produrre word embedding da rappresentazioni di caratteri, supera gli embedding tradizionali e fornisce informazioni sulla sparsità e sul dropout."]} +{"source": "Neural networks with low-precision weights and activations offer compellingefficiency advantages over their full-precision equivalents.The two mostfrequently discussed benefits of quantization are reduced memory consumption,and a faster forward pass when implemented with efficient bitwiseoperations.We propose a third benefit of very low-precision neural networks:improved robustness against some adversarial attacks, and in the worst case,performance that is on par with full-precision models.We focus on the verylow-precision case where weights and activations are both quantized to $\\pm$1,and note that stochastically quantizing weights in just one layer can sharplyreduce the impact of iterative attacks.We observe that non-scaled binary neuralnetworks exhibit a similar effect to the original \\emph{defensive distillation}procedure that led to \\emph{gradient masking}, and a false notion of security.We address this by conducting both black-box and white-box experiments withbinary models that do not artificially mask gradients.", "target": ["Conduciamo attacchi adversarial contro le reti neurali binarizzate e mostriamo che riduciamo l'impatto degli attacchi più forti, mantenendo una precisione comparabile in un setting black-box"]} +{"source": "Unsupervised bilingual dictionary induction (UBDI) is useful for unsupervised machine translation and for cross-lingual transfer of models into low-resource languages.One approach to UBDI is to align word vector spaces in different languages using Generative adversarial networks (GANs) with linear generators, achieving state-of-the-art performance for several language pairs.For some pairs, however, GAN-based induction is unstable or completely fails to align the vector spaces.We focus on cases where linear transformations provably exist, but the performance of GAN-based UBDI depends heavily on the model initialization.We show that the instability depends on the shape and density of the vector sets, but not on noise; it is the result of local optima, but neither over-parameterization nor changing the batch size or the learning rate consistently reduces instability.Nevertheless, we can stabilize GAN-based UBDI through best-of-N model selection, based on an unsupervised stopping criterion.", "target": ["Un'indagine empirica dell'allineamento basato su GAN di spazi vettoriali di parole, concentrandosi sui casi in cui le trasformazioni lineari esistono provatamente, ma il training è instabile."]} +{"source": "Owing to the ubiquity of computer software, software vulnerability detection (SVD) has become an important problem in the software industry and in the field of computer security.One of the most crucial issues in SVD is coping with the scarcity of labeled vulnerabilities in projects that require the laborious manual labeling of code by software security experts.One possible way to address is to employ deep domain adaptation which has recently witnessed enormous success in transferring learning from structural labeled to unlabeled data sources.The general idea is to map both source and target data into a joint feature space and close the discrepancy gap of those data in this joint feature space.Generative adversarial network (GAN) is a technique that attempts to bridge the discrepancy gap and also emerges as a building block to develop deep domain adaptation approaches with state-of-the-art performance.However, deep domain adaptation approaches using the GAN principle to close the discrepancy gap are subject to the mode collapsing problem that negatively impacts the predictive performance.Our aim in this paper is to propose Dual Generator-Discriminator Deep Code Domain Adaptation Network (Dual-GD-DDAN) for tackling the problem of transfer learning from labeled to unlabeled software projects in the context of SVD in order to resolve the mode collapsing problem faced in previous approaches.The experimental results on real-world software projects show that our proposed method outperforms state-of-the-art baselines by a wide margin.", "target": ["Il nostro obiettivo in questo articolo è quello di proporre un nuovo approccio per affrontare il problema del transfer learning da progetti software etichettati a progetti software non etichettati nel contesto di SVD al fine di risolvere il problema del mode collapse affrontato negli approcci precedenti."]} +{"source": "Representation learning is one of the foundations of Deep Learning and allowed important improvements on several Machine Learning tasks, such as Neural Machine Translation, Question Answering and Speech Recognition.Recent works have proposed new methods for learning representations for nodes and edges in graphs.Several of these methods are based on the SkipGram algorithm, and they usually process a large number of multi-hop neighbors in order to produce the context from which node representations are learned.In this paper, we propose an effective and also efficient method for generating node embeddings in graphs that employs a restricted number of permutations over the immediate neighborhood of a node as context to generate its representation, thus ego-centric representations.We present a thorough evaluation showing that our method outperforms state-of-the-art methods in six different datasets related to the problems of link prediction and node classification, being one to three orders of magnitude faster than baselines when generating node embeddings for very large graphs.", "target": ["Un metodo più veloce per generare gli embedding dei nodi che impiega una serie di permutazioni sul neighborhood immediato di un nodo come contesto per generare la sua rappresentazione."]} +{"source": "Orthogonal recurrent neural networks address the vanishing gradient problem by parameterizing the recurrent connections using an orthogonal matrix.This class of models is particularly effective to solve tasks that require the memorization of long sequences.We propose an alternative solution based on explicit memorization using linear autoencoders for sequences.We show how a recently proposed recurrent architecture, the Linear Memory Network, composed of a nonlinear feedforward layer and a separate linear recurrence, can be used to solve hard memorization tasks.We propose an initialization schema that sets the weights of a recurrent architecture to approximate a linear autoencoder of the input sequences, which can be found with a closed-form solution.The initialization schema can be easily adapted to any recurrent architecture. We argue that this approach is superior to a random orthogonal initialization due to the autoencoder, which allows the memorization of long sequences even before training.The empirical analysis show that our approach achieves competitive results against alternative orthogonal models, and the LSTM, on sequential MNIST, permuted MNIST and TIMIT.", "target": ["Mostriamo come inizializzare le architetture ricorrenti con la soluzione in forma chiusa di un autoencoder lineare per sequenze. Mostriamo i vantaggi di questo approccio rispetto alle RNN ortogonali."]} +{"source": "This paper improves upon the line of research that formulates named entity recognition (NER) as a sequence-labeling problem.We use so-called black-box long short-term memory (LSTM) encoders to achieve state-of-the-art results while providing insightful understanding of what the auto-regressive model learns with a parallel self-attention mechanism.Specifically, we decouple the sequence-labeling problem of NER into entity chunking, e.g., Barack_B Obama_E was_O elected_O, and entity typing, e.g., Barack_PERSON Obama_PERSON was_NONE elected_NONE, and analyze how the model learns to, or has difficulties in, capturing text patterns for each of the subtasks.The insights we gain then lead us to explore a more sophisticated deep cross-Bi-LSTM encoder, which proves better at capturing global interactions given both empirical results and a theoretical justification.", "target": ["Forniamo una comprensione approfondita della NER con etichettatura di sequenza e proponiamo di usare due tipi di strutture incrociate, entrambe le quali portano miglioramenti teorici ed empirici."]} +{"source": "Knowledge Graph Embedding (KGE) is the task of jointly learning entity and relation embeddings for a given knowledge graph.Existing methods for learning KGEs can be seen as a two-stage process where(a) entities and relations in the knowledge graph are represented using some linear algebraic structures (embeddings), and(b) a scoring function is defined that evaluates the strength of a relation that holds between two entities using the corresponding relation and entity embeddings.Unfortunately, prior proposals for the scoring functions in the first step have been heuristically motivated, and it is unclear as to how the scoring functions in KGEs relate to the generation process of the underlying knowledge graph.To address this issue, we propose a generative account of the KGE learning task.Specifically, given a knowledge graph represented by a set of relational triples (h, R, t), where the semantic relation R holds between the two entities h (head) and t (tail), we extend the random walk model (Arora et al., 2016a) of word embeddings to KGE.We derive a theoretical relationship between the joint probability p(h, R, t) and the embeddings of h, R and t.Moreover, we show that marginal loss minimisation, a popular objective used by much prior work in KGE, follows naturally from the log-likelihood ratio maximisation under the probabilities estimated from the KGEs according to our theoretical relationship.We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph.The KGEs learnt by our proposed method obtain state-of-the-art performance on FB15K237 and WN18RR benchmark datasets, providing empirical evidence in support of the theory.", "target": ["Presentiamo un modello generativo teoricamente solido di embedding dei knowledge graph."]} +{"source": "Currently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation.Unfortunately, neither of these techniques is applicable to the training of large neural networks due to their large computational and communication overheads.As a scalable technique for shared model governance, we propose splitting deep learning model between multiple parties.This paper empirically investigates the security guarantee of this technique, which is introduced as the problem of model completion: Given the entire training data set or an environment simulator, and a subset of the parameters of a trained deep learning model, how much training is required to recover the model’s original performance? We define a metric for evaluating the hardness of the model completion problem and study it empirically in both supervised learning on ImageNet and reinforcement learning on Atari and DeepMind Lab.Our experiments show that (1) the model completion problem is harder in reinforcement learning than in supervised learning because of the unavailability of the trained agent’s trajectories, and (2) its hardness depends not primarily on the number of parameters of the missing part, but more so on their type and location. Our results suggest that model splitting might be a feasible technique for shared model governance in some settings where training is very expensive.", "target": ["Studiamo empiricamente quanto sia difficile recuperare le parti mancanti dei modelli addestrati"]} +{"source": "This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference.Unlike the existing methods on domain transfer through deep generative models, such as StarGAN (Choi et al., 2017) and UFDN (Liu et al., 2018), the variational domain adaptation has three advantages.Firstly, the samples from the target are not required.Instead, the framework requires one known source as a prior $p(x)$ and binary discriminators, $p(\\mathcal{D}_i|x)$, discriminating the target domain $\\mathcal{D}_i$ from others.Consequently, the framework regards a target as a posterior that can be explicitly formulated through the Bayesian inference, $p(x|\\mathcal{D}_i) \\propto p(\\mathcal{D}_i|x)p(x)$, as exhibited by a further proposed model of dual variational autoencoder (DualVAE).Secondly, the framework is scablable to large-scale domains.As well as VAE encodes a sample $x$ as a mode on a latent space: $\\mu(x) \\in \\mathcal{Z}$, DualVAE encodes a domain $\\mathcal{D}_i$ as a mode on the dual latent space $\\mu^*(\\mathcal{D}_i) \\in \\mathcal{Z}^*$, named domain embedding.It reformulates the posterior with a natural paring $\\langle, \\rangle: \\mathcal{Z} \\times \\mathcal{Z}^* \\rightarrow \\Real$, which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation.Thirdly, DualVAE fastly converges without sophisticated automatic/manual hyperparameter search in comparison to GANs as it requires only one additional parameter to VAE.Through the numerical experiment, we demonstrate the three benefits with multi-domain image generation task on CelebA with up to 60 domains, and exhibits that DualVAE records the state-of-the-art performance outperforming StarGAN and UFDN.", "target": ["Questo articolo propone l'adattamento variazionale del dominio, un framework univoco, scalabile e semplice per l'apprendimento di distribuzioni multiple attraverso l'inferenza variazionale"]} +{"source": "We propose a new method to train neural networks based on a novel combination of adversarial training and provable defenses.The key idea is to model training as a procedure which includes both, the verifier and the adversary.In every iteration, the verifier aims to certify the network using convex relaxation while the adversary tries to find inputs inside that convex relaxation which cause verification to fail.We experimentally show that this training method is promising and achieves the best of both worlds – it produces a model with state-of-the-art accuracy (74.8%) and certified robustness (55.9%) on the challenging CIFAR-10 dataset with a 2/255 L-infinity perturbation.This is a significant improvement over the currently known best results of 68.3% accuracy and 53.9% certified robustness, achieved using a 5 times larger network than our work.", "target": ["Proponiamo una nuova combinazione di training adversarial e difese dimostrabili che produce un modello con accuratezza allo stato dell'arte e robustezza certificata su CIFAR-10."]} +{"source": "Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax.For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered.We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures.In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs.We evaluate our method on two tasks: VarNaming, in which a network attempts to predict the name of a variable given its usage, and VarMisuse, in which the network learns to reason about selecting the correct variable that should be used at a given program location.Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VarMisuse task in many cases.Additionally, our testing showed that VarMisuse identifies a number of bugs in mature open-source projects.", "target": ["I programmi hanno una struttura che può essere rappresentata come grafo, e le reti neurali a grafo possono imparare a trovare i bug su tali grafi"]} +{"source": "Overfitting is an ubiquitous problem in neural network training and usually mitigated using a holdout data set.Here we challenge this rationale and investigate criteria for overfitting without using a holdout data set.Specifically, we train a model for a fixed number of epochs multiple times with varying fractions of randomized labels and for a range of regularization strengths. A properly trained model should not be able to attain an accuracy greater than the fraction of properly labeled data points.Otherwise the model overfits. We introduce two criteria for detecting overfitting and one to detect underfitting.We analyze early stopping, the regularization factor, and network depth.In safety critical applications we are interested in models and parameter settings which perform well and are not likely to overfit.The methods of this paper allow characterizing and identifying such models.", "target": ["Introduciamo e analizziamo diversi criteri per rilevare l'overfitting."]} +{"source": "Partially observable Markov decision processes (POMDPs) are a widely-used framework to model decision-making with uncertainty about the environment and under stochastic outcome.In conventional POMDP models, the observations that the agent receives originate from fixed known distribution.However, in a variety of real-world scenarios the agent has an active role in its perception by selecting which observations to receive.Due to combinatorial nature of such selection process, it is computationally intractable to integrate the perception decision with the planning decision.To prevent such expansion of the action space, we propose a greedy strategy for observation selection that aims to minimize the uncertainty in state. We develop a novel point-based value iteration algorithm that incorporates the greedy strategy to achieve near-optimal uncertainty reduction for sampled belief points.This in turn enables the solver to efficiently approximate the reachable subspace of belief simplex by essentially separating computations related to perception from planning.Lastly, we implement the proposed solver and demonstrate its performance and computational advantage in a range of robotic scenarios where the robot simultaneously performs active perception and planning.", "target": ["Sviluppiamo un solutore di iterazione del valore point-based per POMDP con task di percezione e pianificazione attiva."]} +{"source": "Deep neural networks, in particular convolutional neural networks, have become highly effective tools for compressing images and solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements.This success can be attributed in part to their ability to represent and generate natural images well.Contrary to classical tools such as wavelets, image-generating deep neural networks have a large number of parameters---typically a multiple of their output dimension---and need to be trained on large datasets. In this paper, we propose an untrained simple image model, called the deep decoder, which is a deep neural network that can generate natural images from very few weight parameters.The deep decoder has a simple architecture with no convolutions and fewer weight parameters than the output dimensionality.This underparameterization enables the deep decoder to compress images into a concise set of network weights, which we show is on par with wavelet-based thresholding.Further, underparameterization provides a barrier to overfitting, allowing the deep decoder to have state-of-the-art performance for denoising.The deep decoder is simple in the sense that each layer has an identical structure that consists of only one upsampling unit, pixel-wise linear combination of channels, ReLU activation, and channelwise normalization.This simplicity makes the network amenable to theoretical analysis, and it sheds light on the aspects of neural networks that enable them to form effective signal representations.", "target": ["Introduciamo una deep neural network semplice, non rivoluzionaria e sottoparametrizzata che può, senza training, rappresentare efficacemente le immagini naturali e risolvere task di elaborazione delle immagini come la compressione e il denoising in modo competitivo."]} +{"source": "In this paper we investigate the family of functions representable by deep neural networks (DNN) with rectified linear units (ReLU).We give an algorithm to train a ReLU DNN with one hidden layer to {\\em global optimality} with runtime polynomial in the data size albeit exponential in the input dimension.Further, we improve on the known lower bounds on size (from exponential to super exponential) for approximating a ReLU deep net function by a shallower ReLU net.Our gap theorems hold for smoothly parametrized families of ``hard'' functions, contrary to countable, discrete families known in the literature. An example consequence of our gap theorems is the following: for every natural number $k$ there exists a function representable by a ReLU DNN with $k^2$ hidden layers and total size $k^3$, such that any ReLU DNN with at most $k$ hidden layers will require at least $\\frac12k^{k+1}-1$ total nodes.Finally, for the family of $\\R^n\\to \\R$ DNNs with ReLU activations, we show a new lowerbound on the number of affine pieces, which is larger than previous constructions in certain regimes of the network architecture and most distinctively our lowerbound is demonstrated by an explicit construction of a \\emph{smoothly parameterized} family of functions attaining this scaling.Our construction utilizes the theory of zonotopes from polyhedral theory.", "target": ["Questo articolo 1) caratterizza le funzioni rappresentabili da DNN con ReLU, 2) studia formalmente il beneficio della profondità in tali architetture, 3) fornisce un algoritmo per implementare ERM per l'ottimalità globale per reti ReLU a due strati."]} +{"source": "The backpropagation of error algorithm (BP) is often said to be impossible to implement in a real brain.The recent success of deep networks in machine learning and AI, however, has inspired a number of proposals for understanding how the brain might learn across multiple layers, and hence how it might implement or approximate BP.As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks.Here we present the first results on scaling up a biologically motivated model of deep learning to datasets which need deep networks with appropriate architectures to achieve good performance.We present results on CIFAR-10 and ImageNet. For CIFAR-10 we show that our algorithm, a straightforward, weight-transport-free variant of difference target-propagation (DTP) modified to remove backpropagation from the penultimate layer, is competitive with BP in training deep networks with locally defined receptive fields that have untied weights. For ImageNet we find that both DTP and our algorithm perform significantly worse than BP, opening questions about whether different architectures or algorithms are required to scale these approaches.Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.", "target": ["Benchmark per algoritmi di apprendimento biologicamente plausibili su dataset e architetture complesse"]} +{"source": "Deep neural networks (DNNs) usually contain millions, maybe billions, of parameters/weights, making both storage and computation very expensive.This has motivated a large body of work to reduce the complexity of the neural network by using sparsity-inducing regularizers. Another well-known approach for controlling the complexity of DNNs is parameter sharing/tying, where certain sets of weights are forced to share a common value.Some forms of weight sharing are hard-wired to express certain in- variances, with a notable example being the shift-invariance of convolutional layers.However, there may be other groups of weights that may be tied together during the learning process, thus further re- ducing the complexity of the network.In this paper, we adopt a recently proposed sparsity-inducing regularizer, named GrOWL (group ordered weighted l1), which encourages sparsity and, simulta- neously, learns which groups of parameters should share a common value.GrOWL has been proven effective in linear regression, being able to identify and cope with strongly correlated covariates.Unlike standard sparsity-inducing regularizers (e.g., l1 a.k.a.Lasso), GrOWL not only eliminates unimportant neurons by setting all the corresponding weights to zero, but also explicitly identifies strongly correlated neurons by tying the corresponding weights to a common value.This ability of GrOWL motivates the following two-stage procedure: (i) use GrOWL regularization in the training process to simultaneously identify significant neurons and groups of parameter that should be tied together; (ii) retrain the network, enforcing the structure that was unveiled in the previous phase, i.e., keeping only the significant neurons and enforcing the learned tying structure.We evaluate the proposed approach on several benchmark datasets, showing that it can dramatically compress the network with slight or even no loss on generalization performance.", "target": ["Abbiamo proposto di utilizzare il recente regolatore GrOWL per la simultanea sparsità dei parametri e il tying nell'apprendimento DNN."]} +{"source": "Weight-sharing—the simultaneous optimization of multiple neural networks using the same parameters—has emerged as a key component of state-of-the-art neural architecture search.However, its success is poorly understood and often found to be surprising.We argue that, rather than just being an optimization trick, the weight-sharing approach is induced by the relaxation of a structured hypothesis space, and introduces new algorithmic and theoretical challenges as well as applications beyond neural architecture search.Algorithmically, we show how the geometry of ERM for weight-sharing requires greater care when designing gradient- based minimization methods and apply tools from non-convex non-Euclidean optimization to give general-purpose algorithms that adapt to the underlying structure.We further analyze the learning-theoretic behavior of the bilevel optimization solved by practical weight-sharing methods.Next, using kernel configuration and NLP feature selection as case studies, we demonstrate how weight-sharing applies to the architecture search generalization of NAS and effectively optimizes the resulting bilevel objective.Finally, we use our optimization analysis to develop a simple exponentiated gradient method for NAS that aligns with the underlying optimization geometry and matches state-of-the-art approaches on CIFAR-10.", "target": ["Un'analisi delle strutture di apprendimento e di ottimizzazione della ricerca dell'architettura nelle reti neurali e oltre."]} +{"source": "Deep latent variable models have seen recent success in many data domains.Lossless compression is an application of these models which, despite having the potential to be highly useful, has yet to be implemented in a practical manner.We present '`Bits Back with ANS' (BB-ANS), a scheme to perform lossless compression with latent variable models at a near optimal rate.We demonstrate this scheme by using it to compress the MNIST dataset with a variational auto-encoder model (VAE), achieving compression rates superior to standard methods with only a simple VAE.Given that the scheme is highly amenable to parallelization, we conclude that with a sufficiently high quality generative model this scheme could be used to achieve substantial improvements in compression rate with acceptable running time.We make our implementation available open source at https://github.com/bits-back/bits-back .", "target": ["Facciamo la compressione senza perdite di grandi dataset di immagini usando un VAE, battendo gli algoritmi di compressione esistenti."]} +{"source": "Hyperparameter tuning is arguably the most important ingredient for obtaining state of art performance in deep networks. We focus on hyperparameters that are related to the optimization algorithm, e.g. learning rates, which have a large impact on the training speed and the resulting accuracy.Typically, fixed learning rate schedules are employed during training.We propose Hyperdyn a dynamic hyperparameter optimization method that selects new learning rates on the fly at the end of each epoch.Our explore-exploit framework combines Bayesian optimization (BO) with a rejection strategy, based on a simple probabilistic wait and watch test. We obtain state of art accuracy results on CIFAR and Imagenet datasets, but with significantly faster training, when compared with the best manually tuned networks.", "target": ["Ottimizzazione bayesiana basata sull'ottimizzazione online degli iperparametri."]} +{"source": "Multi-hop text-based question-answering is a current challenge in machine comprehension. This task requires to sequentially integrate facts from multiple passages to answer complex natural language questions.In this paper, we propose a novel architecture, called the Latent Question Reformulation Network (LQR-net), a multi-hop and parallel attentive network designed for question-answering tasks that require reasoning capabilities.LQR-net is composed of an association of \\textbf{reading modules} and \\textbf{reformulation modules}.The purpose of the reading module is to produce a question-aware representation of the document.From this document representation, the reformulation module extracts essential elements to calculate an updated representation of the question.This updated question is then passed to the following hop.We evaluate our architecture on the \\hotpotqa question-answering dataset designed to assess multi-hop reasoning capabilities.Our model achieves competitive results on the public leaderboard and outperforms the best current \\textit{published} models in terms of Exact Match (EM) and $F_1$ score.Finally, we show that an analysis of the sequential reformulations can provide interpretable reasoning paths.", "target": ["In questo articolo, proponiamo la Latent Question Reformulation Network (LQR-net), una rete attenta, multi-hop e parallela, progettata per task di question answering che richiedono capacità di ragionamento."]} +{"source": "We propose a method to automatically compute the importance of features at every observation in time series, by simulating counterfactual trajectories given previous observations.We define the importance of each observation as the change in the model output caused by replacing the observation with a generated one.Our method can be applied to arbitrarily complex time series models.We compare the generated feature importance to existing methods like sensitivity analyses, feature occlusion, and other explanation baselines to show that our approach generates more precise explanations and is less sensitive to noise in the input signals.", "target": ["Spiegare i modelli multivariati di serie temporali trovando osservazioni importanti nel tempo usando i controfattuali"]} +{"source": "This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data.Like much of previous work, we seek to align the learned representations of the source and target domains while preserving discriminability.The way we accomplish alignment is by learning to perform auxiliary self-supervised task(s) on both domains simultaneously. Each self-supervised task brings the two domains closer together along the direction relevant to that task.Training this jointly with the main task classifier on the source domain is shown to successfully generalize to the unlabeled target domain. The presented objective is straightforward to implement and easy to optimize.We achieve state-of-the-art results on four out of seven standard benchmarks, and competitive results on segmentation adaptation.We also demonstrate that our method composes well with another popular pixel-level adaptation method.", "target": ["Usiamo la self-supervision su entrambi i domini per allinearli per un domain adaptation unsupervised."]} +{"source": "We introduce simple, efficient algorithms for computing a MinHash of a probability distribution, suitable for both sparse and dense data, with equivalent running times to the state of the art for both cases.The collision probability of these algorithms is a new measure of the similarity of positive vectors which we investigate in detail.We describe the sense in which this collision probability is optimal for any Locality Sensitive Hash based on sampling.We argue that this similarity measure is more useful for probability distributions than the similarity pursued by other algorithms for weighted MinHash, and is the natural generalization of the Jaccard index.", "target": ["Il minimo di un insieme di hash distribuiti esponenzialmente ha una probabilità di collisione molto utile che generalizza l'indice Jaccard alle distribuzioni di probabilità."]} +{"source": "Recently, progress has been made towards improving relational reasoning in machine learning field.Among existing models, graph neural networks (GNNs) is one of the most effective approaches for multi-hop relational reasoning.In fact, multi-hop relational reasoning is indispensable in many natural language processing tasks such as relation extraction.In this paper, we propose to generate the parameters of graph neural networks (GP-GNNs) according to natural language sentences, which enables GNNs to process relational reasoning on unstructured text inputs.We verify GP-GNNs in relation extraction from text.Experimental results on a human-annotated dataset and two distantly supervised datasets show that our model achieves significant improvements compared to the baselines.We also perform a qualitative analysis to demonstrate that our model could discover more accurate relations by multi-hop relational reasoning.", "target": ["Una graph neural network con parametri generati da linguaggi naturali, che può eseguire ragionamenti a più vie."]} +{"source": "Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks.Normally, the critic’s action-value function is updated using temporal-difference, and the critic in turn provides a loss for the actor that trains it to take actions with higher expected return.In this paper, we introduce a novel and flexible meta-critic that observes the learning process and meta-learns an additional loss for the actor that accelerates and improves actor-critic learning.Compared to the vanilla critic, the meta-critic network is explicitly trained to accelerate the learning process; and compared to existing meta-learning algorithms, meta-critic is rapidly learned online for a single task, rather than slowly over a family of tasks.Crucially, our meta-critic framework is designed for off-policy based learners, which currently provide state-of-the-art reinforcement learning sample efficiency.We demonstrate that online meta-critic learning leads to improvements in a variety of continuous control environments when combined with contemporary Off-PAC methods DDPG, TD3 and the state-of-the-art SAC.", "target": ["Presentiamo Meta-Critic, un modulo critic ausiliario per i metodi off-policy actor-critic che può essere appreso tramite meta-learning online durante l'apprendimento di un singolo task."]} +{"source": "Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data.Nevertheless, these networks often generalize well in practice.It has also been observed that trained networks can often be ``compressed to much smaller representations.The purpose of this paper is to connect these two empirical observations.Our main technical result is a generalization bound for compressed networks based on the compressed size that, combined with off-the-shelf compression algorithms, leads to state-of-the-art generalization guarantees.In particular, we provide the first non-vacuous generalization guarantees for realistic architectures applied to the ImageNet classification problem.Additionally, we show that compressibility of models that tend to overfit is limited.Empirical results show that an increase in overfitting increases the number of bits required to describe a trained network.", "target": ["Otteniamo limiti di generalizzazione non banali sulle deep neural network su scala ImageNet combinando un limite PAC-Bayes originale e un metodo di compressione delle reti neurali disponibili."]} +{"source": "Adversarial examples can be defined as inputs to a model which induce a mistake -- where the model output is different than that of an oracle, perhaps in surprising or malicious ways.Original models of adversarial attacks are primarily studied in the context of classification and computer vision tasks.While several attacks have been proposed in natural language processing (NLP) settings, they often vary in defining the parameters of an attack and what a successful attack would look like.The goal of this work is to propose a unifying model of adversarial examples suitable for NLP tasks in both generative and classification settings.We define the notion of adversarial gain: based in control theory, it is a measure of the change in the output of a system relative to the perturbation of the input (caused by the so-called adversary) presented to the learner.This definition, as we show, can be used under different feature spaces and distance conditions to determine attack or defense effectiveness across different intuitive manifolds.This notion of adversarial gain not only provides a useful way for evaluating adversaries and defenses, but can act as a building block for future work in robustness under adversaries due to its rooted nature in stability and manifold theory.", "target": ["Proponiamo una misura alternativa per determinare l'efficacia degli attacchi adversarial nei modelli NLP secondo un metodo basato su una misura di distanza come L2-gain incrementale nella teoria del controllo."]} +{"source": "We propose a Warped Residual Network (WarpNet) using a parallelizable warp operator for forward and backward propagation to distant layers that trains faster than the original residual neural network.We apply a perturbation theory on residual networks and decouple the interactions between residual units.The resulting warp operator is a first order approximation of the output over multiple layers.The first order perturbation theory exhibits properties such as binomial path lengths and exponential gradient scaling found experimentally by Veit et al (2016). We demonstrate through an extensive performance study that the proposed network achieves comparable predictive performance to the original residual network with the same number of parameters, while achieving a significant speed-up on the total training time.As WarpNet performs model parallelism in residual network training in which weights are distributed over different GPUs, it offers speed-up and capability to train larger networks compared to original residual networks.", "target": ["Proponiamo la Warped Residual Network utilizzando un operatore di curvatura parallelizzabile per forwoard e backward pass verso layer lontani che si allena più velocemente della rete neurale con residual connection originale."]} +{"source": "A plethora of methods attempting to explain predictions of black-box models have been proposed by the Explainable Artificial Intelligence (XAI) community.Yet, measuring the quality of the generated explanations is largely unexplored, making quantitative comparisons non-trivial.In this work, we propose a suite of multifaceted metrics that enables us to objectively compare explainers based on the correctness, consistency, as well as the confidence of the generated explanations.These metrics are computationally inexpensive, do not require model-retraining and can be used across different data modalities.We evaluate them on common explainers such as Grad-CAM, SmoothGrad, LIME and Integrated Gradients.Our experiments show that the proposed metrics reflect qualitative observations reported in earlier works.", "target": ["Proponiamo una suite di metriche che catturano le proprietà desiderate degli algoritmi di explainability e la utilizziamo per confrontare e valutare oggettivamente tali metodi"]} +{"source": "Neural networks are known to produce unexpected results on inputs that are far from the training distribution.One approach to tackle this problem is to detect the samples on which the trained network can not answer reliably.ODIN is a recently proposed method for out-of-distribution detection that does not modify the trained network and achieves good performance for various image classification tasks.In this paper we adapt ODIN for sentence classification and word tagging tasks.We show that the scores produced by ODIN can be used as a confidence measure for the predictions on both in-distribution and out-of-distribution datasets.", "target": ["Un recente metodo di rilevamento di out-of-distribution aiuta a misurare la fiducia delle previsioni RNN per alcuni task NLP"]} +{"source": "Some recent work has shown separation between the expressive power of depth-2 and depth-3 neural networks.These separation results are shown by constructing functions and input distributions, so that the function is well-approximable by a depth-3 neural network of polynomial size but it cannot be well-approximated under the chosen input distribution by any depth-2 neural network of polynomial size.These results are not robust and require carefully chosen functions as well as input distributions.We show a similar separation between the expressive power of depth-2 and depth-3 sigmoidal neural networks over a large class of input distributions, as long as the weights are polynomially bounded.While doing so, we also show that depth-2 sigmoidal neural networks with small width and small weights can be well-approximated by low-degree multivariate polynomials.", "target": ["depth-2-vs-3 separation per reti neurali sigmoidali su distribuzioni generali"]} +{"source": "The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph.In reinforcement learning (RL), where the weighted graph may be interpreted as the state transition process induced by a behavior policy acting on the environment, approximating the eigenvectors of the Laplacian provides a promising approach to state representation learning.However, existing methods for performing this approximation are ill-suited in general RL settings for two main reasons: First, they are computationally expensive, often requiring operations on large matrices.Second, these methods lack adequate justification beyond simple, tabular, finite-state settings.In this paper, we present a fully general and scalable method for approximating the eigenvectors of the Laplacian in a model-free RL context.We systematically evaluate our approach and empirically show that it generalizes beyond the tabular, finite-state setting.Even in tabular, finite-state settings, its ability to approximate the eigenvectors outperforms previous proposals.Finally, we show the potential benefits of using a Laplacian representation learned using our method in goal-achieving RL tasks, providing evidence that our technique can be used to significantly improve the performance of an RL agent.", "target": ["Proponiamo un metodo scalabile per approssimare gli autovettori del Laplaciano nel contesto del reinforcement learning e mostriamo che le rappresentazioni apprese possono migliorare le prestazioni di un agente RL."]} +{"source": "Our work offers a new method for domain translation from semantic label mapsand Computer Graphic (CG) simulation edge map images to photo-realistic im-ages.We train a Generative Adversarial Network (GAN) in a conditional way togenerate a photo-realistic version of a given CG scene.Existing architectures ofGANs still lack the photo-realism capabilities needed to train DNNs for computervision tasks, we address this issue by embedding edge maps, and training it in anadversarial mode.We also offer an extension to our model that uses our GANarchitecture to create visually appealing and temporally coherent videos.", "target": ["Traduzione da simulazione a immagini reali e generazione di video"]} +{"source": "Deep neural networks are widely used in various domains, but the prohibitive computational complexity prevents their deployment on mobile devices.Numerous model compression algorithms have been proposed, however, it is often difficult and time-consuming to choose proper hyper-parameters to obtain an efficient compressed model.In this paper, we propose an automated framework for model compression and acceleration, namely PocketFlow.This is an easy-to-use toolkit that integrates a series of model compression algorithms and embeds a hyper-parameter optimization module to automatically search for the optimal combination of hyper-parameters.Furthermore, the compressed model can be converted into the TensorFlow Lite format and easily deployed on mobile devices to speed-up the inference.PocketFlow is now open-source and publicly available at https://github.com/Tencent/PocketFlow.", "target": ["Proponiamo PocketFlow, un framework automatizzato per la compressione e l'accelerazione dei modelli, per facilitare l'implementazione dei modelli di deep learning sui dispositivi mobili."]} +{"source": "Generative models provide a way to model structure in complex distributions and have been shown to be useful for many tasks of practical interest.However, current techniques for training generative models require access to fully-observed samples.In many settings, it is expensive or even impossible to obtain fully-observed samples, but economical to obtain partial, noisy observations.We consider the task of learning an implicit generative model given only lossy measurements of samples from the distribution of interest.We show that the true underlying distribution can be provably recovered even in the presence of per-sample information loss for a class of measurement models.Based on this, we propose a new method of training Generative Adversarial Networks (GANs) which we call AmbientGAN.On three benchmark datasets, and for various measurement models, we demonstrate substantial qualitative and quantitative improvements.Generative models trained with our method can obtain $2$-$4$x higher inception scores than the baselines.", "target": ["Come addestrare le GAN da osservazioni rumorose, distorte e parziali"]} +{"source": "Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Inception, and smaller models trained from scratch, such as LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly indicate that the empirical spectral density (ESD) of DNN layer matrices displays signatures of traditionally-regularized statistical models, even in the absence of exogenously specifying traditional forms of regularization, such as Dropout or Weight Norm constraints. Building on recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify 5+1 Phases of Training, corresponding to increasing amounts of Implicit Self-Regularization. For smaller and/or older DNNs, this Implicit Self-Regularization is like traditional Tikhonov regularization, in that there is a \"size scale\" separating signal from noise. For state-of-the-art DNNs, however, we identify a novel form of Heavy-Tailed Self-Regularization, similar to the self-organization seen in the statistical physics of disordered systems. This implicit Self-Regularization can depend strongly on the many knobs of the training process. By exploiting the generalization gap phenomena, we demonstrate that we can cause a small model to exhibit all 5+1 phases of training simply by changing the batch size.", "target": ["Vedere l'abstract. (Per la revisione, il paper è identico, tranne che per un materiale supplementare di 59 pagine, che può servire come un technical report stand-along del paper)."]} +{"source": "We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting.The proposed attention mechanism is based on recent methods to visually explain predictions made by DNNs.We apply the proposed explanation-based attention to MNIST and SVHN classification.The conducted experiments show accuracy improvements for the original and class-imbalanced datasets with the same number of training examples and faster long-tail convergence compared to uncertainty-based methods.", "target": ["Introduciamo un meccanismo di attention per migliorare l'estrazione delle feature per deep active learning nel setting semi-supervised."]} +{"source": "We apply canonical forms of gradient complexes (barcodes) to explore neural networks loss surfaces.We present an algorithm for calculations of the objective function's barcodes of minima. Our experiments confirm two principal observations: (1) the barcodes of minima are located in a small lower part of the range of values of objective function and (2) increase of the neural network's depth brings down the minima's barcodes.This has natural implications for the neural network learning and the ability to generalize.", "target": ["Applichiamo forme canoniche di complessi di gradiente (barcode) per esplorare le superfici delle loss delle reti neurali."]} +{"source": "New types of compute hardware in development and entering the market hold the promise of revolutionizing deep learning in a manner as profound as GPUs.However, existing software frameworks and training algorithms for deep learning have yet to evolve to fully leverage the capability of the new wave of silicon.In particular, models that exploit structured input via complex and instance-dependent control flow are difficult to accelerate using existing algorithms and hardware that typically rely on minibatching.We present an asynchronous model-parallel (AMP) training algorithm that is specifically motivated by training on networks of interconnected devices.Through an implementation on multi-core CPUs, we show that AMP training converges to the same accuracy as conventional synchronous training algorithms in a similar number of epochs, but utilizes the available hardware more efficiently, even for small minibatch sizes, resulting in shorter overall training times.Our framework opens the door for scaling up a new class of deep learning models that cannot be efficiently trained today.", "target": ["Utilizzo di aggiornamenti asincroni del gradiente per accelerare il training dinamico delle reti neurali"]} +{"source": "In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem.The global reward signal assigns the same global reward to all agents without distinguishing their contributions, while the local reward signal provides different local rewards to each agent based solely on individual behavior.Both of the two reward assignment approaches have some shortcomings: the former might encourage lazy agents, while the latter might produce selfish agents.In this paper, we study reward design problem in cooperative MARL based on packet routing environments.Firstly, we show that the above two reward signals are prone to produce suboptimal policies.Then, inspired by some observations and considerations, we design some mixed reward signals, which are off-the-shelf to learn better policies.Finally, we turn the mixed reward signals into the adaptive counterparts, which achieve best results in our experiments.Other reward signals are also discussed in this paper.As reward design is a very fundamental problem in RL and especially in MARL, we hope that MARL researchers can rethink the rewards used in their systems.", "target": ["Studiamo il problema della progettazione delle reward nelle MARL cooperative basate su ambienti di routing dei pacchetti. I risultati sperimentali ci ricordano di essere attenti a progettare le reward, poiché sono davvero importanti per guidare il comportamento dell'agente."]} +{"source": "Recent advances have illustrated that it is often possible to learn to solve linear inverse problems in imaging using training data that can outperform more traditional regularized least squares solutions.Along these lines, we present some extensions of the Neumann network, a recently introduced end-to-end learned architecture inspired by a truncated Neumann series expansion of the solution map to a regularized least squares problem.Here we summarize the Neumann network approach, and show that it has a form compatible with the optimal reconstruction function for a given inverse problem.We also investigate an extension of the Neumann network that incorporates a more sample efficient patch-based regularization approach.", "target": ["Le reti di Neumann sono un approccio di apprendimento end-to-end, efficiente in termini di sample, per risolvere problemi inversi lineari nell'imaging che sono compatibili con l'approccio ottimale MSE e ammettono un'estensione all'apprendimento basato su patch."]} +{"source": "End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework.We propose the global-to-local memory pointer (GLMP) networks to address this issue.In our model, a global memory encoder and a local memory decoder are proposed to share external knowledge.The encoder encodes dialogue history, modifies global contextual representation, and generates a global memory pointer.The decoder first generates a sketch response with unfilled slots.Next, it passes the global memory pointer to filter the external knowledge for relevant information, then instantiates the slots via the local memory pointers.We empirically show that our model can improve copy accuracy and mitigate the common out-of-vocabulary problem.As a result, GLMP is able to improve over the previous state-of-the-art models in both simulated bAbI Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on automatic and human evaluation.", "target": ["GLMP: un encoder di memoria globale (context RNN, puntatore globale) e un decoder di memoria locale (sketch RNN, puntatore locale) che condividono la conoscenza esterna (MemNN) sono proposti per rafforzare la generazione di risposte nel task-oriented dialogue."]} +{"source": "The checkerboard phenomenon is one of the well-known visual artifacts in the computer vision field.The origins and solutions of checkerboard artifacts in the pixel space have been studied for a long time, but their effects on the gradient space have rarely been investigated.In this paper, we revisit the checkerboard artifacts in the gradient space which turn out to be the weak point of a network architecture.We explore image-agnostic property of gradient checkerboard artifacts and propose a simple yet effective defense method by utilizing the artifacts.We introduce our defense module, dubbed Artificial Checkerboard Enhancer (ACE), which induces adversarial attacks on designated pixels.This enables the model to deflect attacks by shifting only a single pixel in the image with a remarkable defense rate.We provide extensive experiments to support the effectiveness of our work for various attack scenarios using state-of-the-art attack methods.Furthermore, we show that ACE is even applicable to large-scale datasets including ImageNet dataset and can be easily transferred to various pretrained networks.", "target": ["Proponiamo un nuovo modulo aritificial checkerboard enhancer (ACE) che guida gli attacchi in uno spazio di pixel prestabilito e lo difende con successo con una semplice operazione di padding."]} +{"source": "Min-max formulations have attracted great attention in the ML community due to the rise of deep generative models and adversarial methods, and understanding the dynamics of (stochastic) gradient algorithms for solving such formulations has been a grand challenge.As a first step, we restrict to bilinear zero-sum games and give a systematic analysis of popular gradient updates, for both simultaneous and alternating versions.We provide exact conditions for their convergence and find the optimal parameter setup and convergence rates.In particular, our results offer formal evidence that alternating updates converge \"better\" than simultaneous ones.", "target": ["Analizziamo sistematicamente il comportamento di convergenza di popolari algoritmi basati sul gradiente per la risoluzione di giochi bilineari, con aggiornamenti sia simultanei che alternati."]} +{"source": "Most approaches in generalized zero-shot learning rely on cross-modal mapping between an image feature space and a class embedding space or on generating artificial image features.However, learning a shared cross-modal embedding by aligning the latent spaces of modality-specific autoencoders is shown to be promising in (generalized) zero-shot learning.While following the same direction, we also take artificial feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by aligned variational autoencoders, for the purpose of generating latent features to train a softmax classifier.We evaluate our learned latent features on conventional benchmark datasets and establish a new state of the art on generalized zero-shot as well as on few-shot learning.Moreover, our results on ImageNet with various zero-shot splits show that our latent features generalize well in large-scale settings.", "target": ["Usiamo i VAE per imparare un embedding condiviso dello spazio latente tra le feature e gli attributi dell'immagine e quindi raggiungere risultati allo stato dell'arte nell'apprendimento generalizzato zero-shot."]} +{"source": "Intuitively, image classification should profit from using spatial information.Recent work, however, suggests that this might be overrated in standard CNNs.In this paper, we are pushing the envelope and aim to further investigate the reliance on and necessity of spatial information.We propose and analyze three methods, namely Shuffle Conv, GAP+FC and 1x1 Conv, that destroy spatial information during both training and testing phases.We extensively evaluate these methods on several object recognition datasets (CIFAR100, Small-ImageNet, ImageNet) with a wide range of CNN architectures (VGG16, ResNet50, ResNet152, MobileNet, SqueezeNet).Interestingly, we consistently observe that spatial information can be completely deleted from a significant number of layers with no or only small performance drops.", "target": ["Le informazioni spaziali negli ultimi layer non sono necessarie per una buona precisione di classificazione."]} +{"source": "Disentangling underlying generative factors of a data distribution is important for interpretability and generalizable representations.In this paper, we introduce two novel disentangling methods.Our first method, Unlabeled Disentangling GAN (UD-GAN, unsupervised), decomposes the latent noise by generating similar/dissimilar image pairs and it learns a distance metric on these pairs with siamese networks and a contrastive loss.This pairwise approach provides consistent representations for similar data points.Our second method (UD-GAN-G, weakly supervised) modifies the UD-GAN with user-defined guidance functions, which restrict the information that goes into the siamese networks.This constraint helps UD-GAN-G to focus on the desired semantic variations in the data.We show that both our methods outperform existing unsupervised approaches in quantitative metrics that measure semantic accuracy of the learned representations.In addition, we illustrate that simple guidance functions we use in UD-GAN-G allow us to directly capture the desired variations in the data.", "target": ["Usiamo le reti siamesi per guidare e districare il processo di generazione nelle GAN senza dati annotati."]} +{"source": "We present Predicted Variables, an approach to making machine learning (ML) a first class citizen in programming languages.There is a growing divide in approaches to building systems: using human experts (e.g. programming) on the one hand, and using behavior learned from data (e.g. ML) on the other hand.PVars aim to make using ML in programming easier by hybridizing the two.We leverage the existing concept of variables and create a new type, a predicted variable.PVars are akin to native variables with one important distinction: PVars determine their value using ML when evaluated.We describe PVars and their interface, how they can be used in programming, and demonstrate the feasibility of our approach on three algorithmic problems: binary search, QuickSort, and caches.We show experimentally that PVars are able to improve over the commonly used heuristics and lead to a better performance than the original algorithms.As opposed to previous work applying ML to algorithmic problems, PVars have the advantage that they can be used within the existing frameworks and do not require the existing domain knowledge to be replaced.PVars allow for a seamless integration of ML into existing systems and algorithms.Our PVars implementation currently relies on standard Reinforcement Learning (RL) methods.To learn faster, PVars use the heuristic function, which they are replacing, as an initial function.We show that PVars quickly pick up the behavior of the initial function and then improve performance beyond that without ever performing substantially worse -- allowing for a safe deployment in critical applications.", "target": ["Presentiamo Predicted Variables, un approccio per rendere il machine learning un membro di rilievo dei linguaggi di programmazione."]} +{"source": "Much recent research has been devoted to video prediction and generation, but mostly for short-scale time horizons.The hierarchical video prediction method by Villegas et al. (2017) is an example of a state of the art method for long term video prediction. However, their method has limited applicability in practical settings as it requires a ground truth pose (e.g., poses of joints of a human) at training time. This paper presents a long term hierarchical video prediction model that does not have such a restriction.We show that the network learns its own higher-level structure (e.g., pose equivalent hidden variables) that works better in cases where the ground truth pose does not fully capture all of the information needed to predict the next frame. This method gives sharper results than other video prediction methods which do not require a ground truth pose, and its efficiency is shown on the Humans 3.6M and Robot Pushing datasets.", "target": ["Mostriamo come addestrare un modello di predizione video gerarchico senza bisogno di label di posa."]} +{"source": "Combining information from different sensory modalities to execute goal directed actions is a key aspect of human intelligence.Specifically, human agents are very easily able to translate the task communicated in one sensory domain (say vision) into a representation that enables them to complete this task when they can only sense their environment using a separate sensory modality (say touch).In order to build agents with similar capabilities, in this work we consider the problem of a retrieving a target object from a drawer.The agent is provided with an image of a previously unseen object and it explores objects in the drawer using only tactile sensing to retrieve the object that was shown in the image without receiving any visual feedback.Success at this task requires close integration of visual and tactile sensing.We present a method for performing this task in a simulated environment using an anthropomorphic hand.We hope that future research in the direction of combining sensory signals for acting will find the object retrieval from a drawer to be a useful benchmark problem", "target": ["In questo lavoro, studiamo il problema dell'apprendimento di rappresentazioni per identificare nuovi oggetti esplorando gli oggetti utilizzando il rilevamento tattile. Il punto chiave qui è che la query è fornita nel dominio delle immagini."]} +{"source": "Locality sensitive hashing schemes such as \\simhash provide compact representations of multisets from which similarity can be estimated.However, in certain applications, we need to estimate the similarity of dynamically changing sets. In this case, we need the representation to be a homomorphism so that the hash of unions and differences of sets can be computed directly from the hashes of operands. We propose two representations that have this property for cosine similarity (an extension of \\simhash and angle-preserving random projections), and make substantial progress on a third representation for Jaccard similarity (an extension of \\minhash).We employ these hashes to compress the sufficient statistics of a conditional random field (CRF) coreference model and study how this compression affects our ability to compute similarities as entities are split and merged during inference.\\cut{We study these hashes in a conditional random field (CRF) hierarchical coreference model in order to compute the similarity of entities as they are merged and split during inference.}We also provide novel statistical analysis of \\simhash to help justify it as an estimator inside a CRF, showing that the bias and variance reduce quickly with the number of bits.On a problem of author coreference, we find that our \\simhash scheme allows scaling the hierarchical coreference algorithm by an order of magnitude without degrading its statistical performance or the model's coreference accuracy, as long as we employ at least 128 or 256 bits. Angle-preserving random projections further improve the coreference quality, potentially allowing even fewer dimensions to be used.", "target": ["Impieghiamo schemi di compressione lineare omomorfica per rappresentare le statistiche sufficienti di un conditional random field di coreferenza e questo ci permette di scalare l'inferenza e migliorare la velocità di un ordine di grandezza."]} +{"source": "Motivated by applications to unsupervised learning, we consider the problem of measuring mutual information.Recent analysis has shown that naive kNN estimators of mutual information have serious statistical limitations motivating more refined methods.In this paper we prove that serious statistical limitations are inherent to any measurement method.More specifically, we show that any distribution-free high-confidence lower bound on mutual information cannot be larger than $O(\\ln N)$ where $N$ is the size of the data sample.We also analyze the Donsker-Varadhan lower bound on KL divergence in particular and show that, when simple statistical considerations are taken into account, this bound can never produce a high-confidence value larger than $\\ln N$.While large high-confidence lower bounds are impossible, in practice one can use estimators without formal guarantees.We suggest expressing mutual information as a difference of entropies and using cross entropy as an entropy estimator. We observe that, although cross entropy is only an upper bound on entropy, cross-entropy estimates converge to the true cross entropy at the rate of $1/\\sqrt{N}$.", "target": ["Diamo un'analisi teorica della misura e dell'ottimizzazione dell'informazione reciproca."]} +{"source": "In this paper, we propose a neural network framework called neuron hierarchical network (NHN), that evolves beyond the hierarchy in layers, and concentrates on the hierarchy of neurons.We observe mass redundancy in the weights of both handcrafted and randomly searched architectures.Inspired by the development of human brains, we prune low-sensitivity neurons in the model and add new neurons to the graph, and the relation between individual neurons are emphasized and the existence of layers weakened.We propose a process to discover the best base model by random architecture search, and discover the best locations and connections of the added neurons by evolutionary search.Experiment results show that the NHN achieves higher test accuracy on Cifar-10 than state-of-the-art handcrafted and randomly searched architectures, while requiring much fewer parameters and less searching time.", "target": ["Rompendo la gerarchia dei layer, proponiamo un approccio in 3 fasi per la costruzione di neuron-hierarchy network che superano NAS, SMASH e la rappresentazione gerarchica con meno parametri e con un tempo di ricerca più breve."]} +{"source": "Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire.In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data.In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data.We find that our approach(i) quickly converges to the optimal simulation parameters in controlled experiments and(ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.", "target": ["Proponiamo un algoritmo che regola automaticamente i parametri di un motore di simulazione per generare dati di training per una rete neurale in modo da massimizzare la validation accuracy."]} +{"source": "Modelling statistical relationships beyond the conditional mean is crucial in many settings.Conditional density estimation (CDE) aims to learn the full conditional probability density from data.Though highly expressive, neural network based CDE models can suffer from severe over-fitting when trained with the maximum likelihood objective.Due to the inherent structure of such models, classical regularization approaches in the parameter space are rendered ineffective.To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.We demonstrate that the proposed approach corresponds to a smoothness regularization and prove its asymptotic consistency.In our experiments, noise regularization significantly and consistently outperforms other regularization methods across seven data sets and three CDE models.The effectiveness of noise regularization makes neural network based CDE the preferable method over previous non- and semi-parametric approaches, even when training data is scarce.", "target": ["Uno schema di regolarizzazione model-agnostic per la stima della densità condizionale basata su rete neurale."]} +{"source": "Unsupervised representation learning holds the promise of exploiting large amount of available unlabeled data to learn general representations.A promising technique for unsupervised learning is the framework of Variational Auto-encoders (VAEs).However, unsupervised representations learned by VAEs are significantly outperformed by those learned by supervising for recognition.Our hypothesis is that to learn useful representations for recognition the model needs to be encouraged to learn about repeating and consistent patterns in data.Drawing inspiration from the mid-level representation discovery work, we propose PatchVAE, that reasons about images at patch level.Our key contribution is a bottleneck formulation in a VAE framework that encourages mid-level style representations.Our experiments demonstrate that representations learned by our method perform much better on the recognition tasks compared to those learned by vanilla VAEs.", "target": ["Una formulazione del collo di bottiglia basata su patch in un framework VAE che impara rappresentazioni non supervisionate più adatte al riconoscimento visivo."]} +{"source": "Vanishing and exploding gradients are two of the main obstacles in training deep neural networks, especially in capturing long range dependencies in recurrent neural networks (RNNs).In this paper, we present an efficient parametrization of the transition matrix of an RNN that allows us to stabilize the gradients that arise in its training.Specifically, we parameterize the transition matrix by its singular value decomposition (SVD), which allows us to explicitly track and control its singular values.We attain efficiency by using tools that are common in numerical linear algebra, namely Householder reflectors for representing the orthogonal matrices that arise in the SVD.By explicitly controlling the singular values, our proposed svdRNN method allows us to easily solve the exploding gradient problem and we observe that it empirically solves the vanishing gradient issue to a large extent.We note that the SVD parameterization can be used for any rectangular weight matrix, hence it can be easily extended to any deep neural network, such as a multi-layer perceptron.Theoretically, we demonstrate that our parameterization does not lose any expressive power, and show how it potentially makes the optimization process easier.Our extensive experimental results also demonstrate that the proposed framework converges faster, and has good generalization, especially when the depth is large.", "target": ["Per risolvere i problemi di vanishing/exploding gradient, proponiamo una parametrizzazione efficiente della matrice di transizione di RNN che non perde potenza espressiva, converge più velocemente e ha una buona generalizzazione."]} +{"source": "Recent image style transferring methods achieved arbitrary stylization with input content and style images.To transfer the style of an arbitrary image to a content image, these methods used a feed-forward network with a lowest-scaled feature transformer or a cascade of the networks with a feature transformer of a corresponding scale.However, their approaches did not consider either multi-scaled style in their single-scale feature transformer or dependency between the transformed feature statistics across the cascade networks.This shortcoming resulted in generating partially and inexactly transferred style in the generated images.To overcome this limitation of partial style transfer, we propose a total style transferring method which transfers multi-scaled feature statistics through a single feed-forward process.First, our method transforms multi-scaled feature maps of a content image into those of a target style image by considering both inter-channel correlations in each single scaled feature map and inter-scale correlations between multi-scaled feature maps.Second, each transformed feature map is inserted into the decoder layer of the corresponding scale using skip-connection.Finally, the skip-connected multi-scaled feature maps are decoded into a stylized image through our trained decoder network.", "target": ["Un articolo che suggerisce un metodo per trasformare lo stile delle immagini utilizzando deep neural network."]} +{"source": "Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling.One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill that is trivial for humans.Research in this area is made difficult by the paucity of suitable publicly available datasets both for emotion and dialogues.This work proposes a new task for empathetic dialogue generation and EmpatheticDialogues, a dataset of 25k conversations grounded in emotional situations to facilitate training and evaluating dialogue systems.Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, while improving on other metrics as well (e.g. perceived relevance of responses, BLEU scores), compared to models merely trained on large-scale Internet conversation data.We also present empirical comparisons of several ways to improve the performance of a given model by leveraging existing models or datasets without requiring lengthy re-training of the full model.", "target": ["Miglioriamo i sistemi di dialogo esistenti per rispondere alle persone che condividono storie personali, incorporando rappresentazioni di predizione delle emozioni e rilasciamo anche un nuovo benchmark e un dataset di dialoghi empatici."]} +{"source": "Granger causality is a widely-used criterion for analyzing interactions in large-scale networks.As most physical interactions are inherently nonlinear, we consider the problem of inferring the existence of pairwise Granger causality between nonlinearly interacting stochastic processes from their time series measurements.Our proposed approach relies on modeling the embedded nonlinearities in the measurements using a component-wise time series prediction model based on Statistical Recurrent Units (SRUs).We make a case that the network topology of Granger causal relations is directly inferrable from a structured sparse estimate of the internal parameters of the SRU networks trained to predict the processes’ time series measurements.We propose a variant of SRU, called economy-SRU, which, by design has considerably fewer trainable parameters, and therefore less prone to overfitting.The economy-SRU computes a low-dimensional sketch of its high-dimensional hidden state in the form of random projections to generate the feedback for its recurrent processing.Additionally, the internal weight parameters of the economy-SRU are strategically regularized in a group-wise manner to facilitate the proposed network in extracting meaningful predictive features that are highly time-localized to mimic real-world causal events.Extensive experiments are carried out to demonstrate that the proposed economy-SRU based time series prediction model outperforms the MLP, LSTM and attention-gated CNN-based time series models considered previously for inferring Granger causality.", "target": ["Una nuova architettura di rete neurale ricorrente per rilevare la causalità di Granger a coppie tra serie temporali non linearmente interagenti."]} +{"source": "Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data.However, GCN computes nodes' representation recursively from their neighbors, making the receptive field size grow exponentially with the number of layers. Previous attempts on reducing the receptive field size by subsampling neighbors do not have any convergence guarantee, and their receptive field size per node is still in the order of hundreds.In this paper, we develop a preprocessing strategy and two control variate based algorithms to further reduce the receptive field size.Our algorithms are guaranteed to converge to GCN's local optimum regardless of the neighbor sampling size.Empirical results show that our algorithms have a similar convergence speed per epoch with the exact algorithm even using only two neighbors per node.The time consumption of our algorithm on the Reddit dataset is only one fifth of previous neighbor sampling algorithms.", "target": ["Un algoritmo di training stocastico basato sulla variabile di controllo per graph convolutional network per le quali il campo recettivo può essere solo due vicini per nodo."]} +{"source": "Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption.We propose to apply Monte Carlo methods and importance sampling to sparsify and quantize pre-trained neural networks without any retraining.We obtain sparse, low bit-width integer representations that approximate the full precision weights and activations.The precision, sparsity, and complexity are easily configurable by the amount of sampling performed.Our approach, called Monte Carlo Quantization (MCQ), is linear in both time and space, while the resulting quantized sparse networks show minimal accuracy loss compared to the original full-precision networks.Our method either outperforms or achieves results competitive with methods that do require additional training on a variety of challenging tasks.", "target": ["Metodi Monte Carlo per quantizzare i modelli pre-trained senza alcun training aggiuntivo."]} +{"source": "We propose the Information Maximization Autoencoder (IMAE), an information theoretic approach to simultaneously learn continuous and discrete representations in an unsupervised setting.Unlike the Variational Autoencoder framework, IMAE starts from a stochastic encoder that seeks to map each input data to a hybrid discrete and continuous representation with the objective of maximizing the mutual information between the data and their representations.A decoder is included to approximate the posterior distribution of the data given their representations, where a high fidelity approximation can be achieved by leveraging the informative representations. We show that the proposed objective is theoretically valid and provides a principled framework for understanding the tradeoffs regarding informativeness of each representation factor, disentanglement of representations, and decoding quality.", "target": ["Approccio basato sulla teoria dell'informazione per l'apprendimento unsupervised di un ibrido di rappresentazioni discrete e continue,"]} +{"source": "Learning rules for neural networks necessarily include some form of regularization.Most regularization techniques are conceptualized and implemented in the space of parameters.However, it is also possible to regularize in the space of functions.Here, we propose to measure networks in an $L^2$ Hilbert space, and test a learning rule that regularizes the distance a network can travel through $L^2$-space each update. This approach is inspired by the slow movement of gradient descent through parameter space as well as by the natural gradient, which can be derived from a regularization term upon functional change.The resulting learning rule, which we call Hilbert-constrained gradient descent (HCGD), is thus closely related to the natural gradient but regularizes a different and more calculable metric over the space of functions.Experiments show that the HCGD is efficient and leads to considerably better generalization.", "target": ["È importante considerare l'ottimizzazione nello spazio delle funzioni, non solo nello spazio dei parametri. Introduciamo una regola di apprendimento che riduce la distanza percorsa nello spazio delle funzioni, proprio come SGD limita la distanza percorsa nello spazio dei parametri."]} +{"source": "Stochastic gradient descent (SGD), which dates back to the 1950s, is one of the most popular and effective approaches for performing stochastic optimization.Research on SGD resurged recently in machine learning for optimizing convex loss functions and training nonconvex deep neural networks.The theory assumes that one can easily compute an unbiased gradient estimator, which is usually the case due to the sample average nature of empirical risk minimization.There exist, however, many scenarios (e.g., graphs) where an unbiased estimator may be as expensive to compute as the full gradient because training examples are interconnected.Recently, Chen et al. (2018) proposed using a consistent gradient estimator as an economic alternative.Encouraged by empirical success, we show, in a general setting, that consistent estimators result in the same convergence behavior as do unbiased ones.Our analysis covers strongly convex, convex, and nonconvex objectives.We verify the results with illustrative experiments on synthetic and real-world data.This work opens several new research directions, including the development of more efficient SGD updates with consistent estimators and the design of efficient training algorithms for large-scale graphs.", "target": ["Teoria della convergenza per stimatori di gradiente biased (ma consistent) nell'ottimizzazione stocastica e applicazione alle graph convolutional network"]} +{"source": "We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification.In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand.We demonstrate that such techniques tend to introduce biased estimates for instances whose predictions are supposed to be highly confident.We argue that this deficiency is an artifact of the dynamics of training with SGD-like optimizers, and it has some properties similar to overfitting.Based on this observation, we develop an uncertainty estimation algorithm that selectively estimates the uncertainty of highly confident points, using earlier snapshots of the trained model, before their estimates are jittered (and way before they are ready for actual classification).We present extensive experiments indicating that the proposed algorithm provides uncertainty estimates that are consistently better than all known methods.", "target": ["Usiamo gli snapshot del processo di training per migliorare qualsiasi metodo di stima dell'incertezza di un classificatore DNN."]} +{"source": "Existing public face image datasets are strongly biased toward Caucasian faces, and other races (e.g., Latino) are significantly underrepresented.The models trained from such datasets suffer from inconsistent classification accuracy, which limits the applicability of face analytic systems to non-White race groups.To mitigate the race bias problem in these datasets, we constructed a novel face image dataset containing 108,501 images which is balanced on race.We define 7 race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino.Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.Evaluations were performed on existing face attribute datasets as well as novel image datasets to measure the generalization performance.We find that the model trained from our dataset is substantially more accurate on novel datasets and the accuracy is consistent across race and gender groups.We also compare several commercial computer vision APIs and report their balanced accuracy across gender, race, and age groups.", "target": ["Un nuovo dataset di immagini facciali con equilibrio di razza, sesso ed età che può essere utilizzato per la misurazione e l'attenuazione dei bias"]} +{"source": "Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world.In spite of such advances, a higher level understanding of vision and imagery does not arise from exhaustively modeling an object, but instead identifying higher-level attributes that best summarize the aspects of an object. In this work we attempt to model the drawing process of fonts by building sequential generative models of vector graphics. This model has the benefit of providing a scale-invariant representation for imagery whose latent representation may be systematically manipulated and exploited to perform style propagation.We demonstrate these results on a large dataset of fonts and highlight how such a model captures the statistical dependencies and richness of this dataset.We envision that our model can find use as a tool for designers to facilitate font design.", "target": ["Tentiamo di modellare il processo di disegno dei font costruendo modelli generativi sequenziali di grafica vettoriale (SVGs), una rappresentazione altamente strutturata dei caratteri dei font."]} +{"source": "What can we learn about the functional organization of cortical microcircuits from large-scale recordings of neural activity? To obtain an explicit and interpretable model of time-dependent functional connections between neurons and to establish the dynamics of the cortical information flow, we develop 'dynamic neural relational inference' (dNRI).We study both synthetic and real-world neural spiking data and demonstrate that the developed method is able to uncover the dynamic relations between neurons more reliably than existing baselines.", "target": ["Sviluppiamo \"dynamic neural relational inference\", un modello di autoencoder variazionale che può rappresentare in modo esplicito e interpretabile le relazioni dinamiche nascoste tra i neuroni."]} +{"source": "DeePa is a deep learning framework that explores parallelism in all parallelizable dimensions to accelerate the training process of convolutional neural networks.DeePa optimizes parallelism at the granularity of each individual layer in the network.We present an elimination-based algorithm that finds an optimal parallelism configuration for every layer.Our evaluation shows that DeePa achieves up to 6.5× speedup compared to state-of-the-art deep learning frameworks and reduces data transfers by up to 23×.", "target": ["Per quanto ne sappiamo, DeePa è il primo framework di deep learning che controlla e ottimizza il parallelismo delle CNN in tutte le dimensioni parallelizzabili per ogni layer."]} +{"source": "One can substitute each neuron in any neural network with a kernel machine and obtain a counterpart powered by kernel machines.The new network inherits the expressive power and architecture of the original but works in a more intuitive way since each node enjoys the simple interpretation as a hyperplane (in a reproducing kernel Hilbert space).Further, using the kernel multilayer perceptron as an example, we prove that in classification, an optimal representation that minimizes the risk of the network can be characterized for each hidden layer.This result removes the need of backpropagation in learning the model and can be generalized to any feedforward kernel network.Moreover, unlike backpropagation, which turns models into black boxes, the optimal hidden representation enjoys an intuitive geometric interpretation, making the dynamics of learning in a deep kernel network simple to understand.Empirical results are provided to validate our theory.", "target": ["Combiniamo i metodi kernel con i modelli connessionisti e dimostriamo che le architetture profonde risultanti possono essere addestrate a layer e hanno dinamiche di apprendimento più trasparenti."]} +{"source": "Many notions of fairness may be expressed as linear constraints, and the resulting constrained objective is often optimized by transforming the problem into its Lagrangian dual with additive linear penalties.In non-convex settings, the resulting problem may be difficult to solve as the Lagrangian is not guaranteed to have a deterministic saddle-point equilibrium. In this paper, we propose to modify the linear penalties to second-order ones, and we argue that this results in a more practical training procedure in non-convex, large-data settings.For one, the use of second-order penalties allows training the penalized objective with a fixed value of the penalty coefficient, thus avoiding the instability and potential lack of convergence associated with two-player min-max games.Secondly, we derive a method for efficiently computing the gradients associated with the second-order penalties in stochastic mini-batch settings.Our resulting algorithm performs well empirically, learning an appropriately fair classifier on a number of standard benchmarks.", "target": ["Proponiamo un metodo per ottimizzare stocasticamente le penalità di secondo ordine e mostriamo come questo possa applicarsi al training di classificatori fairness-aware."]} +{"source": "Training methods for deep networks are primarily variants on stochastic gradient descent. Techniques that use (approximate) second-order information are rarely used because of the computational cost and noise associated with those approaches in deep learning contexts. However, in this paper, we show how feedforward deep networks exhibit a low-rank derivative structure. This low-rank structure makes it possible to use second-order information without needing approximations and without incurring a significantly greater computational cost than gradient descent. To demonstrate this capability, we implement Cubic Regularization (CR) on a feedforward deep network with stochastic gradient descent and two of its variants. There, we use CR to calculate learning rates on a per-iteration basis while training on the MNIST and CIFAR-10 datasets. CR proved particularly successful in escaping plateau regions of the objective function. We also found that this approach requires less problem-specific information (e.g. an optimal initial learning rate) than other first-order methods in order to perform well.", "target": ["Mostriamo che le derivate delle reti di deep learning hanno una struttura a basso rango, e questa struttura ci permette di utilizzare le informazioni sulle derivate del secondo ordine per calcolare i learning rate in modo adattivo e in un modo computazionalmente fattibile."]} +{"source": "The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations.Nonetheless, min-max optimization beyond the purpose of AT has not been rigorously explored in the research of adversarial attack and defense.In particular, given a set of risk sources (domains), minimizing the maximal loss induced from the domain set can be reformulated as a general min-max problem that is different from AT.Examples of this general formulation include attacking model ensembles, devising universal perturbation under multiple inputs or data transformations, and generalized AT over different types of attack models.We show that these problems can be solved under a unified and theoretically principled min-max optimization framework. We also show that the self-adjusted domain weights learned from our method provides a means to explain the difficulty level of attack and defense over multiple domains.Extensive experiments show that our approach leads to substantial performance improvement over the conventional averaging strategy.", "target": ["Un framework unificato di ottimizzazione min-max per l'attacco e la difesa adversarial"]} +{"source": "Most deep learning models rely on expressive high-dimensional representations to achieve good performance on tasks such as classification.However, the high dimensionality of these representations makes them difficult to interpret and prone to over-fitting.We propose a simple, intuitive and scalable dimension reduction framework that takes into account the soft probabilistic interpretation of standard deep models for classification.When applying our framework to visualization, our representations more accurately reflect inter-class distances than standard visualization techniques such as t-SNE.We show experimentally that our framework improves generalization performance to unseen categories in zero-shot learning.We also provide a finite sample error upper bound guarantee for the method.", "target": ["riduzione della dimensionalità per i casi in cui gli esempi possono essere rappresentati come distribuzioni soft di probabilità"]} +{"source": "Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments.These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces.However, they have so far assumed that self-generated goals are sampled in a specifically engineered feature space, limiting their autonomy.In this work, we propose an approach using deep representation learning algorithms to learn an adequate goal space.This is a developmental 2-stage approach: first, in a perceptual learning stage, deep learning algorithms use passive raw sensor observations of world changes to learn a corresponding latent space; then goal exploration happens in a second stage by sampling goals in this latent space.We present experiments with a simulated robot arm interacting with an object, and we show that exploration algorithms using such learned representations can closely match, and even sometimes improve, the performance obtained using engineered representations.", "target": ["Proponiamo una nuova architettura di Intrinsically Motivated Goal Exploration con apprendimento unsupervised delle rappresentazioni dello spazio degli obiettivi, e valutiamo come le varie implementazioni permettono la scoperta di una diversità di policy."]} +{"source": "One of the main challenges of deep learning methods is the choice of an appropriate training strategy.In particular, additional steps, such as unsupervised pre-training, have been shown to greatly improve the performances of deep structures.In this article, we propose an extra training step, called post-training, which only optimizes the last layer of the network.We show that this procedure can be analyzed in the context of kernel theory, with the first layers computing an embedding of the data and the last layer a statistical model to solve the task based on this embedding.This step makes sure that the embedding, or representation, of the data is used in the best possible way for the considered task.This idea is then tested on multiple architectures with various data sets, showing that it consistently provides a boost in performance.", "target": ["Proponiamo un ulteriore step di training, chiamato post-training, che calcola i pesi ottimali per l'ultimo layer della rete."]} +{"source": "Natural language processing (NLP) models often require a massive number of parameters for word embeddings, resulting in a large storage or memory footprint.Deploying neural NLP models to mobile devices requires compressing the word embeddings without any significant sacrifices in performance.For this purpose, we propose to construct the embeddings with few basis vectors.For each word, the composition of basis vectors is determined by a hash code.To maximize the compression rate, we adopt the multi-codebook quantization approach instead of binary coding scheme.Each code is composed of multiple discrete numbers, such as (3, 2, 1, 8), where the value of each component is limited to a fixed range.We propose to directly learn the discrete codes in an end-to-end neural network by applying the Gumbel-softmax trick.Experiments show the compression rate achieves 98% in a sentiment analysis task and 94% ~ 99% in machine translation tasks without performance loss.In both tasks, the proposed method can improve the model performance by slightly lowering the compression rate.Compared to other approaches such as character-level segmentation, the proposed method is language-independent and does not require modifications to the network architecture.", "target": ["Comprimere i word embedding oltre il 94% senza danneggiare le prestazioni."]} +{"source": "It is important to detect anomalous inputs when deploying machine learning systems.The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples.At the same time, diverse image and text data are available in enormous quantities.We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE).This enables anomaly detectors to generalize and detect unseen anomalies.In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance.We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue.We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.", "target": ["OE insegna ai rilevatori di anomalie ad apprendere l'euristica per rilevare anomalie non viste; gli esperimenti riguardano la classificazione, la stima della densità e la calibrazione in setting NLP e di visione; non ci accordiamo su sample di distribuzione di test, a differenza del lavoro precedente"]} +{"source": "While generative neural networks can learn to transform a specific input dataset into a specific target dataset, they require having just such a paired set of input/output datasets.For instance, to fool the discriminator, a generative adversarial network (GAN) exclusively trained to transform images of black-haired *men* to blond-haired *men* would need to change gender-related characteristics as well as hair color when given images of black-haired *women* as input.This is problematic, as often it is possible to obtain *a* pair of (source, target) distributions but then have a second source distribution where the target distribution is unknown.The computational challenge is that generative models are good at generation within the manifold of the data that they are trained on.However, generating new samples outside of the manifold or extrapolating \"out-of-sample\" is a much harder problem that has been less well studied.To address this, we introduce a technique called *neuron editing* that learns how neurons encode an edit for a particular transformation in a latent space.We use an autoencoder to decompose the variation within the dataset into activations of different neurons and generate transformed data by defining an editing transformation on those neurons.By performing the transformation in a latent trained space, we encode fairly complex and non-linear transformations to the data with much simpler distribution shifts to the neuron's activations.Our technique is general and works on a wide variety of data domains and applications.We first demonstrate it on image transformations and then move to our two main biological applications: removal of batch artifacts representing unwanted noise and modeling the effect of drug treatments to predict synergy between drugs.", "target": ["Un metodo per imparare una trasformazione tra una coppia di dataset source/target e applicarla a un dataset source separato per il quale non esiste un dataset target"]} +{"source": "In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle.We study the effectiveness of the near- optimal cost-to-go oracle on the planning horizon and demonstrate that the cost- to-go oracle shortens the learner’s planning horizon as function of its accuracy: a globally optimal oracle can shorten the planning horizon to one, leading to a one- step greedy Markov Decision Process which is much easier to optimize, while an oracle that is far away from the optimality requires planning over a longer horizon to achieve near-optimal performance.Hence our new insight bridges the gap and interpolates between imitation learning and reinforcement learning.Motivated by the above mentioned insights, we propose Truncated HORizon Policy Search (THOR), a method that focuses on searching for policies that maximize the total reshaped reward over a finite planning horizon when the oracle is sub-optimal.We experimentally demonstrate that a gradient-based implementation of THOR can achieve superior performance compared to RL baselines and IL baselines even when the oracle is sub-optimal.", "target": ["Combinare l'imitation learning e il reinforcement learning per imparare a superare l'esperto"]} +{"source": "Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions.Most of the existing works implicitly assume that the clean samples from the target distribution are easily available.However, in many applications, this assumption is violated.In this paper, we consider the observation setting in which the samples from a target distribution are given by the superposition of two structured components, and leverage GANs for learning of the structure of the components.We propose a novel framework, demixing-GAN, which learns the distribution of two components at the same time.Through extensive numerical experiments, we demonstrate that the proposed framework can generate clean samples from unknown distributions, which further can be used in demixing of the unseen test images.", "target": ["Un approccio di unsupervised learning per separare due segnali strutturati a partire dalla loro sovrapposizione"]} +{"source": "Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity.They are based on the idea of transforming a simple density into that of the data.We seek to better understand this class of models, and how they compare to previously proposed techniques for generative modeling and unsupervised representation learning.For this purpose we reinterpret NFs in the framework of Variational Autoencoders (VAEs), and present a new form of VAE that generalises normalising flows.The new generalised model also reveals a close connection to denoising autoencoders, and we therefore call our model the Variational Denoising Autoencoder (VDAE).Using our unified model, we systematically examine the model space between flows, variational autoencoders, and denoising autoencoders, in a set of preliminary experiments on the MNIST handwritten digits.The experiments shed light on the modeling assumptions implicit in these models, and they suggest multiple new directions for future research in this space.", "target": ["Esploriamo la relazione tra i normalizing flow e i variational/denoising autoencoder, e proponiamo un nuovo modello che li generalizza."]} +{"source": "We investigate methods to efficiently learn diverse strategies in reinforcement learning for a generative structured prediction problem: query reformulation.In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a final answer.Sub-agents are trained on disjoint partitions of the training data, while the meta-agent is trained on the full training set.Our method makes learning faster, because it is highly parallelizable, and has better generalization performance than strong baselines, such asan ensemble of agents trained on the full data.We evaluate on the tasks of document retrieval and question answering.Theimproved performance seems due to the increased diversity of reformulation strategies.This suggests that multi-agent, hierarchical approaches might play an important role in structured prediction tasks of this kind.However, we also find that it is not obvious how to characterize diversity in this context, and a first attempt based on clustering did not produce good results.Furthermore, reinforcement learning for the reformulation task is hard in high-performance regimes.At best, it only marginally improves over the state of the art, which highlights the complexity of training models in this framework for end-to-end language understanding problems.", "target": ["Usiamo il reinforcement learning per la riformulazione delle query su due task e sorprendentemente troviamo che quando si addestrano più agenti la diversità delle riformulazioni è più importante della specializzazione."]} +{"source": "We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that keep the agent in desirable situations, both during training and at convergence.We formulate these problems as {\\em constrained} Markov decision processes (CMDPs) and present safe policy optimization algorithms that are based on a Lyapunov approach to solve them.Our algorithms can use any standard policy gradient (PG) method, such as deep deterministic policy gradient (DDPG) or proximal policy optimization (PPO), to train a neural network policy, while guaranteeing near-constraint satisfaction for every policy update by projecting either the policy parameter or the selected action onto the set of feasible solutions induced by the state-dependent linearized Lyapunov constraints.Compared to the existing constrained PG algorithms, ours are more data efficient as they are able to utilize both on-policy and off-policy data.Moreover, our action-projection algorithm often leads to less conservative policy updates and allows for natural integration into an end-to-end PG training pipeline.We evaluate our algorithms and compare them with the state-of-the-art baselines on several simulated (MuJoCo) tasks, as well as a real-world robot obstacle-avoidance problem, demonstrating their effectiveness in terms of balancing performance and constraint satisfaction.", "target": ["Un framework generale per incorporare vincoli di sicurezza a lungo termine nel reinforcement learning basato su policy"]} +{"source": "Generative networks are known to be difficult to assess.Recent works on generative models, especially on generative adversarial networks, produce nice samples of varied categories of images.But the validation of their quality is highly dependent on the method used.A good generator should generate data which contain meaningful and varied information and that fit the distribution of a dataset.This paper presents a new method to assess a generator.Our approach is based on training a classifier with a mixture of real and generated samples.We train a generative model over a labeled training set, then we use this generative model to sample new data points that we mix with the original training data.This mixture of real and generated data is thus used to train a classifier which is afterwards tested on a given labeled test dataset.We compare this result with the score of the same classifier trained on the real training data mixed with noise.By computing the classifier's accuracy with different ratios of samples from both distributions (real and generated) we are able to estimate if the generator successfully fits and is able to generalize the distribution of the dataset.Our experiments compare the result of different generators from the VAE and GAN framework on MNIST and fashion MNIST dataset.", "target": ["Valutazione delle reti generative attraverso la loro capacità di data augmentation su modelli discrimativi."]} +{"source": "We propose Automating Science Journalism (ASJ), the process of producing a press release from a scientific paper, as a novel task that can serve as a new benchmark for neural abstractive summarization.ASJ is a challenging task as it requires long source texts to be summarized to long target texts, while also paraphrasing complex scientific concepts to be understood by the general audience.For this purpose, we introduce a specialized dataset for ASJ that contains scientific papers and their press releases from Science Daily.While state-of-the-art sequence-to-sequence (seq2seq) models could easily generate convincing press releases for ASJ, these are generally nonfactual and deviate from the source.To address this issue, we improve seq2seq generation via transfer learning by co-training with new targets:(i) scientific abstracts of sources and(ii) partitioned press releases.We further design a measure for factuality that scores how pertinent to the scientific papers the press releases under our seq2seq models are.Our quantitative and qualitative evaluation shows sizable improvements over a strong baseline, suggesting that the proposed framework could improve seq2seq summarization beyond ASJ.", "target": ["Novità: applicazione della modellazione seq2seq all'automazione del giornalismo scientifico; dataset highly abstractive; trucchi per il transfer learning; misura di valutazione automatica."]} +{"source": "The interpretability of an AI agent's behavior is of utmost importance for effective human-AI interaction.To this end, there has been increasing interest in characterizing and generating interpretable behavior of the agent.An alternative approach to guarantee that the agent generates interpretable behavior would be to design the agent's environment such that uninterpretable behaviors are either prohibitively expensive or unavailable to the agent.To date, there has been work under the umbrella of goal or plan recognition design exploring this notion of environment redesign in some specific instances of interpretable of behavior.In this position paper, we scope the landscape of interpretable behavior and environment redesign in all its different flavors.Specifically, we focus on three specific types of interpretable behaviors -- explicability, legibility, and predictability -- and present a general framework for the problem of environment design that can be instantiated to achieve each of the three interpretable behaviors.We also discuss how specific instantiations of this framework correspond to prior works on environment design and identify exciting opportunities for future work.", "target": ["Presentiamo un approccio per riprogettare l'ambiente in modo che i comportamenti non interpretabili degli agenti siano ridotti al minimo o eliminati."]} +{"source": "Inference models, which replace an optimization-based inference procedure with a learned model, have been fundamental in advancing Bayesian deep learning, the most notable example being variational auto-encoders (VAEs).In this paper, we propose iterative inference models, which learn how to optimize a variational lower bound through repeatedly encoding gradients.Our approach generalizes VAEs under certain conditions, and by viewing VAEs in the context of iterative inference, we provide further insight into several recent empirical findings.We demonstrate the inference optimization capabilities of iterative inference models, explore unique aspects of these models, and show that they outperform standard inference models on typical benchmark data sets.", "target": ["Proponiamo una nuova classe di modelli di inferenza che codificano iterativamente i gradienti per stimare le distribuzioni posteriori approssimate."]} +{"source": "In artificial neural networks trained with gradient descent, the weights used for processing stimuli are also used during backward passes to calculate gradients.For the real brain to approximate gradients, gradient information would have to be propagated separately, such that one set of synaptic weights is used for processing and another set is used for backward passes.This produces the so-called \"weight transport problem\" for biological models of learning, where the backward weights used to calculate gradients need to mirror the forward weights used to process stimuli.This weight transport problem has been considered so hard that popular proposals for biological learning assume that the backward weights are simply random, as in the feedback alignment algorithm.However, such random weights do not appear to work well for large networks.Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem.The resulting algorithm is a special case of an estimator used for causal inference in econometrics, regression discontinuity design.We show empirically that this algorithm rapidly makes the backward weights approximate the forward weights.As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST and CIFAR-10.Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.", "target": ["Presentiamo una regola di apprendimento per i pesi di feedback in una spiking neural network che affronta il problema del trasporto dei pesi."]} +{"source": "Variational inference (VI) methods and especially variational autoencoders (VAEs) specify scalable generative models that enjoy an intuitive connection to manifold learning --- with many default priors the posterior/likelihood pair $q(z|x)$/$p(x|z)$ can be viewed as an approximate homeomorphism (and its inverse) between the data manifold and a latent Euclidean space.However, these approximations are well-documented to become degenerate in training.Unless the subjective prior is carefully chosen, the topologies of the prior and data distributions often will not match.Conversely, diffusion maps (DM) automatically \\textit{infer} the data topology and enjoy a rigorous connection to manifold learning, but do not scale easily or provide the inverse homeomorphism.In this paper, we propose \\textbf{a)} a principled measure for recognizing the mismatch between data and latent distributions and \\textbf{b)} a method that combines the advantages of variational inference and diffusion maps to learn a homeomorphic generative model.The measure, the \\textit{locally bi-Lipschitz property}, is a sufficient condition for a homeomorphism and easy to compute and interpret.The method, the \\textit{variational diffusion autoencoder} (VDAE), is a novel generative algorithm that first infers the topology of the data distribution, then models a diffusion random walk over the data.To achieve efficient computation in VDAEs, we use stochastic versions of both variational inference and manifold learning optimization.We prove approximation theoretic results for the dimension dependence of VDAEs, and that locally isotropic sampling in the latent space results in a random walk over the reconstructed manifold.Finally, we demonstrate the utility of our method on various real and synthetic datasets, and show that it exhibits performance superior to other generative models.", "target": ["Combiniamo l'inferenza variazionale e l'apprendimento del manifold (in particolare VAE e mappe di diffusione) per costruire un modello generativo basato su un cammino casuale di diffusione su un manifold di dati; generiamo sample attingendo dalla distribuzione stazionaria del cammino."]} +{"source": "While deep learning and deep reinforcement learning systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge, particularly as these algorithms learn individual tasks from scratch.Multi-task learning has emerged as a promising approach for sharing structure across multiple tasks to enable more efficient learning.However, the multi-task setting presents a number of optimization challenges, making it difficult to realize large efficiency gains compared to learning tasks independently.The reasons why multi-task learning is so challenging compared to single task learning are not fully understood.Motivated by the insight that gradient interference causes optimization challenges, we develop a simple and general approach for avoiding interference between gradients from different tasks, by altering the gradients through a technique we refer to as “gradient surgery”.We propose a form of gradient surgery that projects the gradient of a task onto the normal plane of the gradient of any other task that has a conflicting gradient.On a series of challenging multi-task supervised and multi-task reinforcement learning problems, we find that this approach leads to substantial gains in efficiency and performance. Further, it can be effectively combined with previously-proposed multi-task architectures for enhanced performance in a model-agnostic way.", "target": ["Sviluppiamo un approccio semplice e generale per evitare l'interferenza tra i gradienti di diversi task, che migliora le prestazioni del multi-task learning in entrambi i domini di supervised learning e reinforcement learning."]} +{"source": "In this paper we propose to view the acceptance rate of the Metropolis-Hastings algorithm as a universal objective for learning to sample from target distribution -- given either as a set of samples or in the form of unnormalized density.This point of view unifies the goals of such approaches as Markov Chain Monte Carlo (MCMC), Generative Adversarial Networks (GANs), variational inference.To reveal the connection we derive the lower bound on the acceptance rate and treat it as the objective for learning explicit and implicit samplers.The form of the lower bound allows for doubly stochastic gradient optimization in case the target distribution factorizes (i.e. over data points).We empirically validate our approach on Bayesian inference for neural networks and generative models for images.", "target": ["Learning to sample tramite il limite inferiore del tasso di accettazione dell'algoritmo Metropolis-Hastings"]} +{"source": "This paper proposes a self-supervised learning approach for video features that results in significantly improved performance on downstream tasks (such as video classification, captioning and segmentation) compared to existing methods.Our method extends the BERT model for text sequences to the case of sequences of real-valued feature vectors, by replacing the softmax loss with noise contrastive estimation (NCE).We also show how to learn representations from sequences of visual features and sequences of words derived from ASR (automatic speech recognition), and show that such cross-modal training (when possible) helps even more.", "target": ["BERT generalizzato per input continui e cross-modali; rappresentazioni video self-supervised allo stato dell'arte."]} +{"source": "We present a generic dynamic architecture that employs a problem specific differentiable forking mechanism to leverage discrete logical information about the problem data structure.We adapt and apply our model to CLEVR Visual Question Answering, giving rise to the DDRprog architecture; compared to previous approaches, our model achieves higher accuracy in half as many epochs with five times fewer learnable parameters.Our model directly models underlying question logic using a recurrent controller that jointly predicts and executes functional neural modules; it explicitly forks subprocesses to handle logical branching.While FiLM and other competitive models are static architectures with less supervision, we argue that inclusion of program labels enables learning of higher level logical operations -- our architecture achieves particularly high performance on questions requiring counting and integer comparison. We further demonstrate the generality of our approach though DDRstack -- an application of our method to reverse Polish notation expression evaluation in which the inclusion of a stack assumption allows our approach to generalize to long expressions, significantly outperforming an LSTM with ten times as many learnable parameters.", "target": ["Un'architettura dinamica generica che impiega un meccanismo di forking differenziabile specifico del problema per codificare ipotesi hard di strutture di dati. Applicato a CLEVR VQA e alla valutazione delle espressioni."]} +{"source": "We propose Support-guided Adversarial Imitation Learning (SAIL), a generic imitation learning framework that unifies support estimation of the expert policy with the family of Adversarial Imitation Learning (AIL) algorithms.SAIL addresses two important challenges of AIL, including the implicit reward bias and potential training instability.We also show that SAIL is at least as efficient as standard AIL.In an extensive evaluation, we demonstrate that the proposed method effectively handles the reward bias and achieves better performance and training stability than other baseline methods on a wide range of benchmark control tasks.", "target": ["Unifichiamo la stima del supporto con la famiglia degli algoritmi di Adversarial Imitation Learning nel Support-guided Adversarial Imitation Learning, un framework di imitation learning più robusto e stabile."]} +{"source": "We consider the task of few shot link prediction, where the goal is to predict missing edges across multiple graphs using only a small sample of known edges.We show that current link prediction methods are generally ill-equipped to handle this task---as they cannot effectively transfer knowledge between graphs in a multi-graph setting and are unable to effectively learn from very sparse data.To address this challenge, we introduce a new gradient-based meta learning framework, Meta-Graph, that leverages higher-order gradients along with a learned graph signature function that conditionally generates a graph neural network initialization.Using a novel set of few shot link prediction benchmarks, we show that Meta-Graph enables not only fast adaptation but also better final convergence and can effectively learn using only a small sample of true edges.", "target": ["Applichiamo il meta-learning basato sul gradiente al dominio dei grafi e introduciamo una nuova funzione di transfer specifica per i grafi per effettuare un ulteriore bootstrapping del processo."]} +{"source": "Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set.However, a determinate input distribution as well as a specific architecture of neural networks may impose limitations on capturing the diversity in the high dimensional target space.To resolve this difficulty, we propose a training framework that greedily produce a series of generative adversarial networks that incrementally capture the diversity of the target space.We show theoretically and empirically that our training algorithm converges to the theoretically optimal distribution, the projection of the real distribution onto the convex hull of the network's distribution space.", "target": ["Proponiamo un nuovo metodo per addestrare incrementalmente un modello generativo con mixture per approssimare la proiezione delle informazioni della distribuzione dei dati reali."]} +{"source": "Generative priors have become highly effective in solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements.With a generative model we can represent an image with a much lower dimensional latent codes.In the context of compressive sensing, if the unknown image belongs to the range of a pretrained generative network, then we can recover the image by estimating the underlying compact latent code from the available measurements.However, recent studies revealed that even untrained deep neural networks can work as a prior for recovering natural images.These approaches update the network weights keeping latent codes fixed to reconstruct the target image from the given measurements.In this paper, we optimize over network weights and latent codes to use untrained generative network as prior for video compressive sensing problem.We show that by optimizing over latent code, we can additionally get concise representation of the frames which retain the structural similarity of the video frames.We also apply low-rank constraint on the latent codes to represent the video sequences in even lower dimensional latent space.We empirically show that our proposed methods provide better or comparable accuracy and low computational complexity compared to the existing methods.", "target": ["Recuperare i video da misure compressive imparando una rappresentazione a bassa dimensione (low-rank) direttamente dalle misure mentre si allena un deep generator."]} +{"source": "Magnitude-based pruning is one of the simplest methods for pruning neural networks.Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable performances for pruning modern architectures.Based on the observation that the magnitude-based pruning indeed minimizes the Frobenius distortion of a linear operator corresponding to a single layer, we develop a simple pruning method, coined lookahead pruning, by extending the single layer optimization to a multi-layer optimization.Our experimental results demonstrate that the proposed method consistently outperforms the magnitude pruning on various networks including VGG and ResNet, particularly in the high-sparsity regime.", "target": ["Studiamo una generalizzazione multilayer del pruning basato sulla magnitudine."]} +{"source": "Recent literature has demonstrated promising results on the training of Generative Adversarial Networks by employing a set of discriminators, as opposed to the traditional game involving one generator against a single adversary.Those methods perform single-objective optimization on some simple consolidation of the losses, e.g. an average.In this work, we revisit the multiple-discriminator approach by framing the simultaneous minimization of losses provided by different models as a multi-objective optimization problem.Specifically, we evaluate the performance of multiple gradient descent and the hypervolume maximization algorithm on a number of different datasets.Moreover, we argue that the previously proposed methods and hypervolume maximization can all be seen as variations of multiple gradient descent in which the update direction computation can be done efficiently.Our results indicate that hypervolume maximization presents a better compromise between sample quality and diversity, and computational cost than previous methods.", "target": ["Introduciamo la massimizzazione dell'ipervolume per il training di GAN con discriminatori multipli, mostrando miglioramenti delle prestazioni in termini di qualità e diversità del sample."]} +{"source": "Designing of search space is a critical problem for neural architecture search (NAS) algorithms.We propose a fine-grained search space comprised of atomic blocks, a minimal search unit much smaller than the ones used in recent NAS algorithms.This search space facilitates direct selection of channel numbers and kernel sizes in convolutions.In addition, we propose a resource-aware architecture search algorithm which dynamically selects atomic blocks during training.The algorithm is further accelerated by a dynamic network shrinkage technique.Instead of a search-and-retrain two-stage paradigm, our method can simultaneously search and train the target architecture in an end-to-end manner. Our method achieves state-of-the-art performance under several FLOPS configurations on ImageNet with a negligible searching cost.We open our entire codebase at: https://github.com/meijieru/AtomNAS.", "target": ["Un nuovo stato dell'arte su Imagenet per il setting mobile"]} +{"source": "We introduce Lyceum, a high-performance computational ecosystem for robotlearning. Lyceum is built on top of the Julia programming language and theMuJoCo physics simulator, combining the ease-of-use of a high-level program-ming language with the performance of native C. Lyceum is up to 10-20Xfaster compared to other popular abstractions like OpenAI’sGymand Deep-Mind’sdm-control. This substantially reduces training time for various re-inforcement learning algorithms; and is also fast enough to support real-timemodel predictive control with physics simulators. Lyceum has a straightfor-ward API and supports parallel computation across multiple cores or machines.The code base, tutorials, and demonstration videos can be found at: https://sites.google.com/view/lyceum-anon.", "target": ["Un framework per la simulazione robotica ad alte prestazioni e per lo sviluppo di algoritmi."]} +{"source": "There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift.Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA).Furthermore, almost all the prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for incremental, real-time adaptation.Devoid of such highly impractical assumptions, we propose a novel two-stage learning process.Initially, in the procurement-stage, the objective is to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift.To achieve this, we enhance the model’s ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework.Subsequently, in the deployment-stage, the objective is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples.To achieve this, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighing mechanism, named as Source Similarity Metric (SSM).A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches.", "target": ["Un nuovo paradigma di domain adaptation unsupervised - che esegue l'adattamento senza accedere ai dati di origine ('source-free') e senza alcuna assunzione circa il gap di categoria tra source e target ('universale')."]} +{"source": "One of the long-standing challenges in Artificial Intelligence for learning goal-directed behavior is to build a single agent which can solve multiple tasks.Recent progress in multi-task learning for goal-directed sequential problems has been in the form of distillation based learning wherein a student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks.While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large expert networks which require extensive data and computation time for training.In this work, we propose an efficient multi-task learning framework which solves multiple goal-directed tasks in an on-line setup without the need for expert supervision.Our work uses active learning principles to achieve multi-task learning by sampling the harder tasks more than the easier ones.We propose three distinct models under our active sampling framework.An adaptive method with extremely competitive multi-tasking performance.A UCB-based meta-learner which casts the problem of picking the next task to train on as a multi-armed bandit problem.A meta-learning method that casts the next-task picking problem as a full Reinforcement Learning problem and uses actor-critic methods for optimizing the multi-tasking performance directly.We demonstrate results in the Atari 2600 domain on seven multi-tasking instances: three 6-task instances, one 8-task instance, two 12-task instances and one 21-task instance.", "target": ["Lasciare che un meta-learner decida il task su cui addestrare un agente in un ambiente multi-task migliora sostanzialmente la capacità di multi-tasking"]} +{"source": "Numerous machine reading comprehension (MRC) datasets often involve manual annotation, requiring enormous human effort, and hence the size of the dataset remains significantly smaller than the size of the data available for unsupervised learning.Recently, researchers proposed a model for generating synthetic question-and-answer data from large corpora such as Wikipedia.This model is utilized to generate synthetic data for training an MRC model before fine-tuning it using the original MRC dataset.This technique shows better performance than other general pre-training techniques such as language modeling, because the characteristics of the generated data are similar to those of the downstream MRC data.However, it is difficult to have high-quality synthetic data comparable to human-annotated MRC datasets.To address this issue, we propose Answer-containing Sentence Generation (ASGen), a novel pre-training method for generating synthetic data involving two advanced techniques, (1) dynamically determining K answers and (2) pre-training the question generator on the answer-containing sentence generation task.We evaluate the question generation capability of our method by comparing the BLEU score with existing methods and test our method by fine-tuning the MRC model on the downstream MRC data after training on synthetic data.Experimental results show that our approach outperforms existing generation methods and increases the performance of the state-of-the-art MRC models across a range of MRC datasets such as SQuAD-v1.1, SQuAD-v2.0, KorQuAD and QUASAR-T without any architectural modifications to the original MRC model.", "target": ["Proponiamo Answer-containing Sentence Generation (ASGen), un nuovo metodo di pre-training per generare dati sintetici per la comprensione automatica della lettura."]} +{"source": "Deep neural networks (DNNs) dominate current research in machine learning.Due to massive GPU parallelization DNN training is no longer a bottleneck, and large models with many parameters and high computational effort lead common benchmark tables.In contrast, embedded devices have a very limited capability.As a result, both model size and inference time must be significantly reduced if DNNs are to achieve suitable performance on embedded devices.We propose a soft quantization approach to train DNNs that can be evaluated using pure fixed-point arithmetic.By exploiting the bit-shift mechanism, we derive fixed-point quantization constraints for all important components, including batch normalization and ReLU.Compared to floating-point arithmetic, fixed-point calculations significantly reduce computational effort whereas low-bit representations immediately decrease memory costs.We evaluate our approach with different architectures on common benchmark data sets and compare with recent quantization approaches.We achieve new state of the art performance using 4-bit fixed-point models with an error rate of 4.98% on CIFAR-10.", "target": ["Approccio di quantizzazione soft per imparare rappresentazioni pure e fixed-point di deep neural network"]} +{"source": "We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks.Existing approaches conventionally learn full model parameters independently and then compress them via \\emph{ad hoc} processing such as model pruning or filter factorization.Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces {parameter sharing} throughout the learning process.We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably.By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters.Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification.Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet.Combined with weight quantization, the resulted models are up to \\textbf{180$\\times$} smaller and theoretically up to \\textbf{16$\\times$} faster than the well-established baselines, without noticeable performance drop.", "target": ["Presentiamo una nuova architettura di rete per l'apprendimento di deep neural network compatte ed efficienti"]} +{"source": "Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.However, in such visual perception pipeline the detected objects must also be tracked, in a process called Multiple Object Tracking (MOT), to build the moving trajectories of surrounding obstacles.Since MOT is designed to be robust against errors in object detection, it poses a general challenge to existing attack techniques that blindly target objection detection: we find that a success rate of over 98% is needed for them to actually affect the tracking results, a requirement that no existing attack technique can satisfy.In this paper, we are the first to study adversarial machine learning attacks against the complete visual perception pipeline in autonomous driving, and discover a novel attack technique, tracker hijacking, that can effectively fool MOT using AEs on object detection.Using our technique, successful AEs on as few as one single frame can move an existing object in to or out of the headway of an autonomous vehicle to cause potential safety hazards.We perform evaluation using the Berkeley Deep Drive dataset and find that on average when 3 frames are attacked, our attack can have a nearly 100% success rate while attacks that blindly target object detection only have up to 25%.", "target": ["Studiamo per la prima volta gli adversarial attack di machine learning contro i meccanismi di Multiple Object Tracking."]} +{"source": "Self-supervised learning (SlfSL), aiming at learning feature representations through ingeniously designed pretext tasks without human annotation, has achieved compelling progress in the past few years.Very recently, SlfSL has also been identified as a promising solution for semi-supervised learning (SemSL) since it offers a new paradigm to utilize unlabeled data.This work further explores this direction by proposing a new framework to seamlessly couple SlfSL with SemSL.Our insight is that the prediction target in SemSL can be modeled as the latent factor in the predictor for the SlfSL target.Marginalizing over the latent factor naturally derives a new formulation which marries the prediction targets of these two learning processes.By implementing this framework through a simple-but-effective SlfSL approach -- rotation angle prediction, we create a new SemSL approach called Conditional Rotation Angle Prediction (CRAP).Specifically, CRAP is featured by adopting a module which predicts the image rotation angle \\textbf{conditioned on the candidate image class}.Through experimental evaluation, we show that CRAP achieves superior performance over the other existing ways of combining SlfSL and SemSL.Moreover, the proposed SemSL framework is highly extendable.By augmenting CRAP with a simple SemSL technique and a modification of the rotation angle prediction task, our method has already achieved the state-of-the-art SemSL performance.", "target": ["Accoppiare l'apprendimento semi-supervised con l'apprendimento self-supervised e modellare esplicitamente il task self-supervised condizionato da quello semi-supervised"]} +{"source": "Ranking is a central task in machine learning and information retrieval.In this task, it is especially important to present the user with a slate of items that is appealing as a whole.This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the decision of which other items should be chosen alongside it.In this work, we propose a sequence-to-sequence model for ranking called seq2slate.At each step, the model predicts the next item to place on the slate given the items already chosen.The recurrent nature of the model allows complex dependencies between items to be captured directly in a flexible and scalable way.We show how to learn the model end-to-end from weak supervision in the form of easily obtained click-through data.We further demonstrate the usefulness of our approach in experiments on standard ranking benchmarks as well as in a real-world recommendation system.", "target": ["Un'architettura di pointer network per il re-ranking degli articoli, appresa dai log dei click-through."]} +{"source": "While momentum-based methods, in conjunction with the stochastic gradient descent, are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods.In practice, the momentum parameter is often chosen in a heuristic fashion with little theoretical guidance.In this work, we use the framework of algorithmic stability to provide an upper-bound on the generalization error for the class of strongly convex loss functions, under mild technical assumptions.Our bound decays to zero inversely with the size of the training set, and increases as the momentum parameter is increased.We also develop an upper-bound on the expected true risk, in terms of the number of training steps, the size of the training set, and the momentum parameter.", "target": ["Il metodo dello stochastic gradient con momentum è capace di generalizzare."]} +{"source": "Imitation learning from demonstrations usually relies on learning a policy from trajectories of optimal states and actions.However, in real life expert demonstrations, often the action information is missing and only state trajectories are available.We present a model-based imitation learning method that can learn environment-specific optimal actions only from expert state trajectories.Our proposed method starts with a model-free reinforcement learning algorithm with a heuristic reward signal to sample environment dynamics, which is then used to train the state-transition probability.Subsequently, we learn the optimal actions from expert state trajectories by supervised learning, while back-propagating the error gradients through the modeled environment dynamics.Experimental evaluations show that our proposed method successfully achieves performance similar to (state, action) trajectory-based traditional imitation learning methods even in the absence of action information, with much fewer iterations compared to conventional model-free reinforcement learning methods.We also demonstrate that our method can learn to act from only video demonstrations of expert agent for simple games and can learn to achieve desired performance in less number of iterations.", "target": ["Imparare a imitare un esperto in assenza di azioni ottimali imparando al tempo stesso un modello dinamico durante l'esplorazione dell'ambiente."]} +{"source": "Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the original model with commensurate training steps.While this discovery is insightful, finding proper sub-networks requires iterative training and pruning.The high cost incurred limits the applications of the lottery ticket hypothesis.We show there exists a subset of the aforementioned sub-networks that converge significantly faster during the training process and thus can mitigate the cost issue.We conduct extensive experiments to show such sub-networks consistently exist across various model structures for a restrictive setting of hyperparameters (e.g., carefully selected learning rate, pruning ratio, and model capacity). As a practical application of our findings, we demonstrate that such sub-networks can help in cutting down the total time of adversarial training, a standard approach to improve robustness, by up to 49% on CIFAR-10 to achieve the state-of-the-art robustness.", "target": ["Mostriamo la possibilità del pruning per trovare una piccola sottorete con un tasso di convergenza significativamente più alto del modello completo."]} +{"source": "Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc.We consider the problem of unsupervised learning of disentangled representations from large pool of unlabeled observations, and propose a variational inference based approach to infer disentangled latent factors.We introduce a regularizer on the expectation of the approximate posterior over observed data that encourages the disentanglement.We also propose a new disentanglement metric which is better aligned with the qualitative disentanglement observed in the decoder's output.We empirically observe significant improvement over existing methods in terms of both disentanglement and data likelihood (reconstruction quality).", "target": ["Proponiamo un approccio basato sull'inferenza variazionale per favorire l'inferenza di latent disentangled. Proponiamo anche una nuova metrica per quantificare il disentanglement."]} +{"source": "In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution.Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents.Moreover, the environmental stochasticity and uncertainties increase exponentially with the increase in the number of agents.Previous works borrow various multiagent coordination mechanisms into deep learning architecture to facilitate multiagent coordination.However, none of them explicitly consider action semantics between agents that different actions have different influences on other agents.In this paper, we propose a novel network architecture, named Action Semantics Network (ASN), that explicitly represents such action semantics between agents.ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.ASN can be easily combined with existing deep reinforcement learning (DRL) algorithms to boost their performance.Experimental results on StarCraft II micromanagement and Neural MMO show ASN significantly improves the performance of state-of-the-art DRL approaches compared with several network architectures.", "target": ["La nostra proposta ASN caratterizza l'influenza di diverse azioni su altri agenti usando reti neurali basate sulla semantica delle azioni tra di loro."]} +{"source": "Spiking neural networks are being investigated both as biologically plausible models of neural computation and also as a potentially more efficient type of neural network.While convolutional spiking neural networks have been demonstrated to achieve near state-of-the-art performance, only one solution has been proposed to convert gated recurrent neural networks, so far.Recurrent neural networks in the form of networks of gating memory cells have been central in state-of-the-art solutions in problem domains that involve sequence recognition or generation.Here, we design an analog gated LSTM cell where its neurons can be substituted for efficient stochastic spiking neurons.These adaptive spiking neurons implement an adaptive form of sigma-delta coding to convert internally computed analog activation values to spike-trains.For such neurons, we approximate the effective activation function, which resembles a sigmoid.We show how analog neurons with such activation functions can be used to create an analog LSTM cell; networks of these cells can then be trained with standard backpropagation.We train these LSTM networks on a noisy and noiseless version of the original sequence prediction task from Hochreiter & Schmidhuber (1997), and also on a noisy and noiseless version of a classical working memory reinforcement learning task, the T-Maze.Substituting the analog neurons for corresponding adaptive spiking neurons, we then show that almost all resulting spiking neural network equivalents correctly compute the original tasks.", "target": ["Mostriamo una spiking neural network asincrona, ricorrente e gated che corrisponde a un'unità LSTM."]} +{"source": "CNNs are widely successful in recognizing human actions in videos, albeit with a great cost of computation.This cost is significantly higher in the case of long-range actions, where a video can span up to a few minutes, on average.The goal of this paper is to reduce the computational cost of these CNNs, without sacrificing their performance.We propose VideoEpitoma, a neural network architecture comprising two modules: a timestamp selector and a video classifier.Given a long-range video of thousands of timesteps, the selector learns to choose only a few but most representative timesteps for the video.This selector resides on top of a lightweight CNN such as MobileNet and uses a novel gating module to take a binary decision: consider or discard a video timestep.This decision is conditioned on both the timestep-level feature and the video-level consensus.A heavyweight CNN model such as I3D takes the selected frames as input and performs video classification.Using off-the-shelf video classifiers, VideoEpitoma reduces the computation by up to 50\\% without compromising the accuracy.In addition, we show that if trained end-to-end, the selector learns to make better choices to the benefit of the classifier, despite the selector and the classifier residing on two different CNNs.Finally, we report state-of-the-art results on two datasets for long-range action recognition: Charades and Breakfast Actions, with much-reduced computation.In particular, we match the accuracy of I3D by using less than half of the computation.", "target": ["Classificazione video efficiente utilizzando un modulo di gating condizionale basato sui frame per selezionare i frame più dominanti, seguito dalla modellazione temporale e dal classificatore."]} +{"source": "Human annotation for syntactic parsing is expensive, and large resources are available only for a fraction of languages.A question we ask is whether one can leverage abundant unlabeled texts to improve syntactic parsers, beyond just using the texts to obtain more generalisable lexical features (i.e. beyond word embeddings).To this end, we propose a novel latent-variable generative model for semi-supervised syntactic dependency parsing.As exact inference is intractable, we introduce a differentiable relaxation to obtain approximate samples and compute gradients with respect to the parser parameters.Our method (Differentiable Perturb-and-Parse) relies on differentiable dynamic programming over stochastically perturbed edge scores.We demonstrate effectiveness of our approach with experiments on English, French and Swedish.", "target": ["Programmazione dinamica differenziabile su pesi di ingresso perturbati con applicazione al VAE semi-supervised"]} +{"source": "DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks.We show that these methods do not produce the theoretically correct explanation for a linear model.Yet they are used on multi-layer networks with millions of parameters.This is a cause for concern since linear models are simple neural networks.We argue that explanation methods for neural nets should work reliably in the limit of simplicity, the linear models.Based on our analysis of linear models we propose a generalization that yields two explanation techniques (PatternNet and PatternAttribution) that are theoretically sound for linear models and produce improved explanations for deep networks.", "target": ["Senza apprendimento, è impossibile spiegare le decisioni di un modello di machine learning."]} +{"source": "Graph neural networks have shown promising results on representing and analyzing diverse graph-structured data such as social, citation, and protein interaction networks.Existing approaches commonly suffer from the oversmoothing issue, regardless of whether policies are edge-based or node-based for neighborhood aggregation.Most methods also focus on transductive scenarios for fixed graphs, leading to poor generalization performance for unseen graphs.To address these issues, we propose a new graph neural network model that considers both edge-based neighborhood relationships and node-based entity features, i.e. Graph Entities with Step Mixture via random walk (GESM).GESM employs a mixture of various steps through random walk to alleviate the oversmoothing problem and attention to use node information explicitly.These two mechanisms allow for a weighted neighborhood aggregation which considers the properties of entities and relations.With intensive experiments, we show that the proposed GESM achieves state-of-the-art or comparable performances on four benchmark graph datasets comprising transductive and inductive learning tasks.Furthermore, we empirically demonstrate the significance of considering global information.The source code will be publicly available in the near future.", "target": ["Una semplice ed efficace graph neural network con un mix di step di una random walk e attention"]} +{"source": "Basis pursuit is a compressed sensing optimization in which the l1-norm is minimized subject to model error constraints.Here we use a deep neural network prior instead of l1-regularization.Using known noise statistics, we jointly learn the prior and reconstruct images without access to ground-truth data.During training, we use alternating minimization across an unrolled iterative network and jointly solve for the neural network weights and training set image reconstructions.At inference, we fix the weights and pass the measurements through the network.We compare reconstruction performance between unsupervised and supervised (i.e. with ground-truth) methods.We hypothesize this technique could be used to learn reconstruction when ground-truth data are unavailable, such as in high-resolution dynamic MRI.", "target": ["Presentiamo una ricostruzione non supervisionata con deep learning per problemi inversi di imaging che combina reti neurali con vincoli basati su modelli."]} +{"source": "Deep learning approaches usually require a large amount of labeled data to generalize.However, humans can learn a new concept only by a few samples.One of the high cogntition human capablities is to learn several concepts at the same time.In this paper, we address the task of classifying multiple objects by seeing only a few samples from each category.To the best of authors' knowledge, there is no dataset specially designed for few-shot multiclass classification.We design a task of mutli-object few class classification and an environment for easy creating controllable datasets for this task.We demonstrate that the proposed dataset is sound using a method which is an extension of prototypical networks.", "target": ["Introduciamo un task diagnostico che è una variazione dell'apprendimento few shot e introduciamo un dataset per esso."]} +{"source": "We present a new approach to defining a sequence loss function to train a summarizer by using a secondary encoder-decoder as a loss function, alleviating a shortcoming of word level training for sequence outputs.The technique is based on the intuition that if a summary is a good one, it should contain the most essential information from the original article, and therefore should itself be a good input sequence, in lieu of the original, from which a summary can be generated.We present experimental results where we apply this additional loss function to a general abstractive summarizer on a news summarization dataset.The result is an improvement in the ROUGE metric and an especially large improvement in human evaluations, suggesting enhanced performance that is competitive with specialized state-of-the-art models.", "target": ["Presentiamo l'uso di un encoder-decoder secondario come loss per aiutare ad addestrare un modello di summarization."]} +{"source": "Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent.In this work, we propose a novel unsupervised video-to-video translation model.Our model decomposes the style and the content, uses specialized encoder-decoder structure and propagates the inter-frame information through bidirectional recurrent neural network (RNN) units.The style-content decomposition mechanism enables us to achieve long-term style-consistent video translation results as well as provides us with a good interface for modality flexible translation.In addition, by changing the input frames and style codes incorporated in our translation, we propose a video interpolation loss, which captures temporal information within the sequence to train our building blocks in a self-supervised manner.Our model can produce photo-realistic, spatio-temporal consistent translated videos in a multimodal way.Subjective and objective experimental results validate the superiority of our model over the existing methods.", "target": ["Un framework di traduzione video-to-video unsupervised, coerente dal punto di vista temporale e flessibile dal punto di vista della modalità, addestrato in modo self-supervised."]} +{"source": "Providing transparency of AI planning systems is crucial for their success in practical applications.In order to create a transparent system, a user must be able to query it for explanations about its outputs.We argue that a key underlying principle for this is the use of causality within a planning model, and that argumentation frameworks provide an intuitive representation of such causality.In this paper, we discuss how argumentation can aid in extracting causalities in plans and models, and how they can create explanations from them.", "target": ["I framework di argomentazione sono usati per rappresentare la causalità dei piani/modelli da utilizzare per le spiegazioni."]} +{"source": "Point clouds, as a form of Lagrangian representation, allow for powerful and flexible applications in a large number of computational disciplines.We propose a novel deep-learning method to learn stable and temporally coherent feature spaces for points clouds that change over time.We identify a set of inherent problems with these approaches: without knowledge of the time dimension, the inferred solutions can exhibit strong flickering, and easy solutions to suppress this flickering can result in undesirable local minima that manifest themselves as halo structures.We propose a novel temporal loss function that takes into account higher time derivatives of the point positions, and encourages mingling, i.e., to prevent the aforementioned halos.We combine these techniques in a super-resolution method with a truncation approach to flexibly adapt the size of the generated positions.We show that our method works for large, deforming point sets from different sources to demonstrate the flexibility of our approach.", "target": ["Proponiamo un approccio con rete neurale generativa per nuvole di punti temporalmente coerenti."]} +{"source": "We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available.We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and demonstrate that the current state-of-the-art methods are optimal in a natural sense.Despite this optimality, we show how to improve black-box attacks by bringing a new element into the problem: gradient priors.We give a bandit optimization-based algorithm that allows us to seamlessly integrate any such priors, and we explicitly identify and incorporate two examples.The resulting methods use two to four times fewer queries and fail two to five times less than the current state-of-the-art.The code for reproducing our work is available at https://git.io/fAjOJ.", "target": ["Presentiamo una visione unificante sugli adversarial attack black-box come un problema di stima del gradiente, e poi presentiamo un framework (basato su bandit optimization) per integrare i prior nella stima del gradiente, portando a prestazioni significativamente aumentate."]} +{"source": "Collecting high-quality, large scale datasets typically requires significant resources.The aim of the present work is to improve the label efficiency of large neural networks operating on audio data through multitask learning with self-supervised tasks on unlabeled data.To this end, we trained an end-to-end audio feature extractor based on WaveNet that feeds into simple, yet versatile task-specific neural networks.We describe three self-supervised learning tasks that can operate on any large, unlabeled audio corpus.We demonstrate that, in a scenario with limited labeled training data, one can significantly improve the performance of a supervised classification task by simultaneously training it with these additional self-supervised tasks.We show that one can improve performance on a diverse sound events classification task by nearly 6\\% when jointly trained with up to three distinct self-supervised tasks.This improvement scales with the number of additional auxiliary tasks as well as the amount of unsupervised data.We also show that incorporating data augmentation into our multitask setting leads to even further gains in performance.", "target": ["Migliorare l'efficienza delle label attraverso multi-task learning su dati uditivi"]} +{"source": "Information need of humans is essentially multimodal in nature, enabling maximum exploitation of situated context.We introduce a dataset for sequential procedural (how-to) text generation from images in cooking domain.The dataset consists of 16,441 cooking recipes with 160,479 photos associated with different steps.We setup a baseline motivated by the best performing model in terms of human evaluation for the Visual Story Telling (ViST) task.In addition, we introduce two models to incorporate high level structure learnt by a Finite State Machine (FSM) in neural sequential generation process by: (1) Scaffolding Structure in Decoder (SSiD) (2) Scaffolding Structure in Loss (SSiL).These models show an improvement in empirical as well as human evaluation.Our best performing model (SSiL) achieves a METEOR score of 0.31, which is an improvement of 0.6 over the baseline model.We also conducted human evaluation of the generated grounded recipes, which reveal that 61% found that our proposed (SSiL) model is better than the baseline model in terms of overall recipes, and 72.5% preferred our model in terms of coherence and structure.We also discuss analysis of the output highlighting key important NLP issues for prospective directions.", "target": ["L'articolo presenta due tecniche per incorporare una struttura di alto livello nella generazione di testo procedurale da una sequenza di immagini."]} +{"source": "Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real-world problems tackled by machine learning and a hot topic of current research.Some examples include data simulators that are widely used in engineering and scientific research, generative adversarial networks (GANs) for image synthesis, and hot-off-the-press approximate inference techniques relying on implicit distributions.The majority of existing approaches to learning implicit models rely on approximating the intractable distribution or optimisation objective for gradient-based optimisation, which is liable to produce inaccurate updates and thus poor models.This paper alleviates the need for such approximations by proposing the \\emph{Stein gradient estimator}, which directly estimates the score function of the implicitly defined distribution.The efficacy of the proposed estimator is empirically demonstrated by examples that include meta-learning for approximate inference and entropy regularised GANs that provide improved sample diversity.", "target": ["Abbiamo introdotto un nuovo stimatore di gradiente utilizzando il metodo di Stein, e confrontato con altri metodi sull'apprendimento di modelli impliciti per l'inferenza approssimativa e la generazione di immagini."]} +{"source": "Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks.This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures.Noise models that are distributed with different density functions are given common magnitude levels via Structural Similarity (SSIM) metric in order to create an appropriate ground for comparison.The basic results are conforming with the most of the common notions in machine learning, and also introduces some novel heuristics and recommendations on noise injection.The new approaches will provide better understanding on optimal learning procedures for image classification.", "target": ["Metodologia ideale per iniettare rumore nei dati di input durante il training di CNN"]} +{"source": "State-of-the-art Unsupervised Domain Adaptation (UDA) methods learn transferable features by minimizing the feature distribution discrepancy between the source and target domains.Different from these methods which do not model the feature distributions explicitly, in this paper, we explore explicit feature distribution modeling for UDA.In particular, we propose Distribution Matching Prototypical Network (DMPN) to model the deep features from each domain as Gaussian mixture distributions.With explicit feature distribution modeling, we can easily measure the discrepancy between the two domains.In DMPN, we propose two new domain discrepancy losses with probabilistic interpretations.The first one minimizes the distances between the corresponding Gaussian component means of the source and target data.The second one minimizes the pseudo negative log likelihood of generating the target features from source feature distribution.To learn both discriminative and domain invariant features, DMPN is trained by minimizing the classification loss on the labeled source data and the domain discrepancy losses together.Extensive experiments are conducted over two UDA tasks.Our approach yields a large margin in the Digits Image transfer task over state-of-the-art approaches.More remarkably, DMPN obtains a mean accuracy of 81.4% on VisDA 2017 dataset.The hyper-parameter sensitivity analysis shows that our approach is robust w.r.t hyper-parameter changes.", "target": ["Proponiamo di modellare esplicitamente le distribuzioni di feature profonde dei dati source e target come distribuzioni di misture gaussiane per l'Unsupervised Domain Adaptation (UDA) e di ottenere risultati superiori in più task UDA rispetto ai metodi all'avanguardia."]} +{"source": "Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents. We propose to decompose a complex environment using a task-agnostic world graphs, an abstraction that accelerates learning by enabling agents to focus exploration on a subspace of the environment.The nodes of a world graph are important waypoint states and edges represent feasible traversals between them. Our framework has two learning phases: 1) identifying world graph nodes and edges by training a binary recurrent variational auto-encoder (VAE) on trajectory data and2) a hierarchical RL framework that leverages structural and connectivity knowledge from the learned world graph to bias exploration towards task-relevant waypoints and regions.We show that our approach significantly accelerates RL on a suite of challenging 2D grid world tasks: compared to baselines, world graph integration doubles achieved rewards on simpler tasks, e.g. MultiGoal, and manages to solve more challenging tasks, e.g. Door-Key, where baselines fail.", "target": ["Impariamo un'astrazione del world graph dell'ambiente indipendente dal task e mostriamo come usarlo per l'esplorazione strutturata può accelerare significativamente il RL per il downstream task."]} +{"source": "We introduce the notion of property signatures, a representation for programs andprogram specifications meant for consumption by machine learning algorithms.Given a function with input type τ_in and output type τ_out, a property is a functionof type: (τ_in, τ_out) → Bool that (informally) describes some simple propertyof the function under consideration.For instance, if τ_in and τ_out are both listsof the same type, one property might ask ‘is the input list the same length as theoutput list?’.If we have a list of such properties, we can evaluate them all for ourfunction to get a list of outputs that we will call the property signature.Crucially,we can ‘guess’ the property signature for a function given only a set of input/outputpairs meant to specify that function.We discuss several potential applications ofproperty signatures and show experimentally that they can be used to improveover a baseline synthesizer so that it emits twice as many programs in less thanone-tenth of the time.", "target": ["Rappresentiamo un programma per computer usando un insieme di programmi più semplici e usiamo questa rappresentazione per migliorare le tecniche di sintesi dei programmi."]} +{"source": "Social dilemmas are situations where individuals face a temptation to increase their payoffs at a cost to total welfare.Building artificially intelligent agents that achieve good outcomes in these situations is important because many real world interactions include a tension between selfish interests and the welfare of others.We show how to modify modern reinforcement learning methods to construct agents that act in ways that are simple to understand, nice (begin by cooperating), provokable (try to avoid being exploited), and forgiving (try to return to mutual cooperation).We show both theoretically and experimentally that such agents can maintain cooperation in Markov social dilemmas.Our construction does not require training methods beyond a modification of self-play, thus if an environment is such that good strategies can be constructed in the zero-sum case (eg. Atari) then we can construct agents that solve social dilemmas in this environment.", "target": ["Come possiamo costruire agenti artificiali che risolvano i dilemmi sociali (situazioni in cui gli individui affrontano la tentazione di aumentare i loro payoff riducendo il benessere comune)?"]} +{"source": "The reparameterization trick has become one of the most useful tools in the field of variational inference.However, the reparameterization trick is based on the standardization transformation which restricts the scope of application of this method to distributions that have tractable inverse cumulative distribution functions or are expressible as deterministic transformations of such distributions.In this paper, we generalized the reparameterization trick by allowing a general transformation.Unlike other similar works, we develop the generalized transformation-based gradient model formally and rigorously.We discover that the proposed model is a special case of control variate indicating that the proposed model can combine the advantages of CV and generalized reparameterization.Based on the proposed gradient model, we propose a new polynomial-based gradient estimator which has better theoretical performance than the reparameterization trick under certain condition and can be applied to a larger class of variational distributions.In studies of synthetic and real data, we show that our proposed gradient estimator has a significantly lower gradient variance than other state-of-the-art methods thus enabling a faster inference procedure.", "target": ["Proponiamo un nuovo modello di gradiente generalizzato basato sulla trasformazione e proponiamo uno stimatore di gradiente polinomiale basato sul modello."]} +{"source": "The fault diagnosis in a modern communication system is traditionally supposed to be difficult, or even impractical for a purely data-driven machine learning approach, for it is a humanmade system of intensive knowledge.A few labeled raw packet streams extracted from fault archive can hardly be sufficient to deduce the intricate logic of underlying protocols.In this paper, we supplement these limited samples with two inexhaustible data sources: the unlabeled records probed from a system in service, and the labeled data simulated in an emulation environment.To transfer their inherent knowledge to the target domain, we construct a directed information flow graph, whose nodes are neural network components consisting of two generators, three discriminators and one classifier, and whose every forward path represents a pair of adversarial optimization goals, in accord with the semi-supervised and transfer learning demands.The multi-headed network can be trained in an alternative approach, at each iteration of which we select one target to update the weights along the path upstream, and refresh the residual layer-wisely to all outputs downstream.The actual results show that it can achieve comparable accuracy on classifying Transmission Control Protocol (TCP) streams without deliberate expert features.The solution has relieved operation engineers from massive works of understanding and maintaining rules, and provided a quick solution independent of specific protocols.", "target": ["semi-supervised e transfer learning sulla classificazione del flusso di pacchetti, tramite un sistema di blocchi neurali cooperativi o avversari"]} +{"source": "Our work addresses two important issues with recurrent neural networks: (1) they are over-parameterized, and (2) the recurrent weight matrix is ill-conditioned.The former increases the sample complexity of learning and the training time.The latter causes the vanishing and exploding gradient problem.We present a flexible recurrent neural network model called Kronecker Recurrent Units (KRU).KRU achieves parameter efficiency in RNNs through a Kronecker factored recurrent matrix.It overcomes the ill-conditioning of the recurrent matrix by enforcing soft unitary constraints on the factors.Thanks to the small dimensionality of the factors, maintaining these constraints is computationally efficient.Our experimental results on seven standard data-sets reveal that KRU can reduce the number of parameters by three orders of magnitude in the recurrent weight matrix compared to the existing recurrent models, without trading the statistical performance.These results in particular show that while there are advantages in having a high dimensional recurrent space, the capacity of the recurrent part of the model can be dramatically reduced.", "target": ["Il nostro lavoro presenta una fattorizzazione di Kronecker delle matrici di peso ricorrenti per reti neurali ricorrenti efficienti e ben condizionate."]} +{"source": "This paper studies the undesired phenomena of over-sensitivity of representations learned by deep networks to semantically-irrelevant changes in data.We identify a cause for this shortcoming in the classical Variational Auto-encoder (VAE) objective, the evidence lower bound (ELBO).We show that the ELBO fails to control the behaviour of the encoder out of the support of the empirical data distribution and this behaviour of the VAE can lead to extreme errors in the learned representation.This is a key hurdle in the effective use of representations for data-efficient learning and transfer.To address this problem, we propose to augment the data with specifications that enforce insensitivity of the representation with respect to families of transformations.To incorporate these specifications, we propose a regularization method that is based on a selection mechanism that creates a fictive data point by explicitly perturbing an observed true data point.For certain choices of parameters, our formulation naturally leads to the minimization of the entropy regularized Wasserstein distance between representations.We illustrate our approach on standard datasets and experimentally show that significant improvements in the downstream adversarial accuracy can be achieved by learning robust representations completely in an unsupervised manner, without a reference to a particular downstream task and without a costly supervised adversarial training procedure.", "target": ["Proponiamo un metodo per calcolare rappresentazioni adversarially robust in un modo completamente unsupervised."]} +{"source": "We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data.We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks.This extension allows to model complex interactions while being more global in its search compared to other greedy approaches.In addition to comparing our method to existing continuous optimization methods, we provide missing empirical comparisons to nonlinear greedy search methods.On both synthetic and real-world data sets, this new method outperforms current continuous methods on most tasks while being competitive with existing greedy search methods on important metrics for causal inference.", "target": ["Proponiamo un nuovo approccio basato sullo score per l'apprendimento strutturale/causale sfruttando le reti neurali e una recente formulazione continua vincolata a questo problema"]} +{"source": "We study the problem of designing provably optimal adversarial noise algorithms that induce misclassification in settings where a learner aggregates decisions from multiple classifiers.Given the demonstrated vulnerability of state-of-the-art models to adversarial examples, recent efforts within the field of robust machine learning have focused on the use of ensemble classifiers as a way of boosting the robustness of individual models.In this paper, we design provably optimal attacks against a set of classifiers.We demonstrate how this problem can be framed as finding strategies at equilibrium in a two player, zero sum game between a learner and an adversary and consequently illustrate the need for randomization in adversarial attacks.The main technical challenge we consider is the design of best response oracles that can be implemented in a Multiplicative Weight Updates framework to find equilibrium strategies in the zero-sum game.We develop a series of scalable noise generation algorithms for deep neural networks, and show that it outperforms state-of-the-art attacks on various image classification tasks.Although there are generally no guarantees for deep learning, we show this is a well-principled approach in that it is provably optimal for linear classifiers.The main insight is a geometric characterization of the decision space that reduces the problem of designing best response oracles to minimizing a quadratic function over a set of convex polytopes.", "target": ["Il paper analizza il problema della progettazione di attacchi adversarial contro classificatori multipli, introducendo algoritmi che sono ottimali per classificatori lineari e che forniscono risultati allo stato dell'arte per deep learning."]} +{"source": "Multiagent systems where the agents interact among themselves and with an stochastic environment can be formalized as stochastic games.We study a subclass of these games, named Markov potential games (MPGs), that appear often in economic and engineering applications when the agents share some common resource.We consider MPGs with continuous state-action variables, coupled constraints and nonconvex rewards.Previous analysis followed a variational approach that is only valid for very simple cases (convex rewards, invertible dynamics, and no coupled constraints); or considered deterministic dynamics and provided open-loop (OL) analysis, studying strategies that consist in predefined action sequences, which are not optimal for stochastic environments.We present a closed-loop (CL) analysis for MPGs and consider parametric policies that depend on the current state and where agents adapt to stochastic transitions.We provide easily verifiable, sufficient and necessary conditions for a stochastic game to be an MPG, even for complex parametric functions (e.g., deep neural networks); and show that a closed-loop Nash equilibrium (NE) can be found (or at least approximated) by solving a related optimal control problem (OCP).This is useful since solving an OCP---which is a single-objective problem---is usually much simpler than solving the original set of coupled OCPs that form the game---which is a multiobjective control problem.This is a considerable improvement over the previously standard approach for the CL analysis of MPGs, which gives no approximate solution if no NE belongs to the chosen parametric family, and which is practical only for simple parametric forms.We illustrate the theoretical contributions with an example by applying our approach to a noncooperative communications engineering game.We then solve the game with a deep reinforcement learning algorithm that learns policies that closely approximates an exact variational NE of the game.", "target": ["Presentiamo un'analisi generale a ciclo chiuso per i giochi potenziali di Markov e mostriamo che il deep reinforcement learning può essere usato per imparare l'equilibrio di Nash approssimato a ciclo chiuso."]} +{"source": "We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model.We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based 'Bits-Back with ANS' algorithm for lossless compression to large color photographs, and achieving state of the art for compression of full size ImageNet images.We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results.", "target": ["Scaliamo la compressione senza perdita con variabili latenti, battendo gli approcci esistenti su immagini ImageNet a grandezza naturale."]} +{"source": "State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input.In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image.Despite substantial research interest, the cause of the phenomenon is still poorly understood and remains unsolved.We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold.As a first step towards exploring this hypothesis, we study a simple synthetic dataset of classifying between two concentric high dimensional spheres.For this dataset we show a fundamental tradeoff between the amount of test error and the average distance to nearest error.In particular, we prove that any model which misclassifies a small constant fraction of a sphere will be vulnerable to adversarial perturbations of size $O(1/\\sqrt{d})$.Surprisingly, when we train several different architectures on this dataset, all of their error sets naturally approach this theoretical bound.As a result of the theory, the vulnerability of neural networks to small adversarial perturbations is a logical consequence of the amount of test error observed.We hope that our theoretical analysis of this very simple case will point the way forward to explore how the geometry of complex real-world data sets leads to adversarial examples.", "target": ["Noi ipotizziamo che la vulnerabilità degli image model a piccole perturbazioni adversarial sia un risultato naturale della geometria ad alta dimensione del manifold di dati. Esploriamo e dimostriamo teoricamente questa ipotesi per un semplice dataset sintetico."]} +{"source": "It has been established that diverse behaviors spanning the controllable subspace of a Markov decision process can be trained by rewarding a policy for being distinguishable from other policies.However, one limitation of this formulation is the difficulty to generalize beyond the finite set of behaviors being explicitly learned, as may be needed in subsequent tasks.Successor features provide an appealing solution to this generalization problem, but require defining the reward function as linear in some grounded feature space.In this paper, we show that these two techniques can be combined, and that each method solves the other's primary limitation.To do so we introduce Variational Intrinsic Successor FeatuRes (VISR), a novel algorithm which learns controllable features that can be leveraged to provide enhanced generalization and fast task inference through the successor features framework.We empirically validate VISR on the full Atari suite, in a novel setup wherein the rewards are only exposed briefly after a long unsupervised phase.Achieving human-level performance on 12 games and beating all baselines, we believe VISR represents a step towards agents that rapidly learn from limited feedback.", "target": ["Introduciamo Variational Intrinsic Successor FeatuRes (VISR), un nuovo algoritmo che impara feature controllabili che possono essere sfruttate per fornire un'inferenza veloce dei task attraverso il framework delle feature del successore."]} +{"source": "Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn strong supervised models like convolutional neural networks.However, these models trained on one data domain may not generalize well to other domains unequipped with annotations for model finetuning.To avoid the labor-intensive process of annotation, we develop a domain adaptation method to adapt the source data to the unlabeled target domain.To this end, we propose to learn discriminative feature representations of patches based on label histograms in the source domain, through the construction of a disentangled space.With such representations as guidance, we then use an adversarial learning scheme to push the feature representations in target patches to the closer distributions in source ones.In addition, we show that our framework can integrate a global alignment process with the proposed patch-level alignment and achieve state-of-the-art performance on semantic segmentation.Extensive ablation studies and experiments are conducted on numerous benchmark datasets with various settings, such as synthetic-to-real and cross-city scenarios.", "target": ["Un metodo di domain adaptation per l'output strutturato tramite l'apprendimento di rappresentazioni di feature discriminanti a livello di patch"]} +{"source": "Many machine learning algorithms are vulnerable to almost imperceptible perturbations of their inputs.So far it was unclear how much risk adversarial perturbations carry for the safety of real-world machine learning applications because most methods used to generate such perturbations rely either on detailed model information (gradient-based attacks) or on confidence scores such as class probabilities (score-based attacks), neither of which are available in most real-world scenarios.In many such cases one currently needs to retreat to transfer-based attacks which rely on cumbersome substitute models, need access to the training data and can be defended against.Here we emphasise the importance of attacks which solely rely on the final model decision.Such decision-based attacks are (1) applicable to real-world black-box models such as autonomous cars, (2) need less knowledge and are easier to apply than transfer-based attacks and (3) are more robust to simple defences than gradient- or score-based attacks.Previous attacks in this category were limited to simple models or simple datasets.Here we introduce the Boundary Attack, a decision-based attack that starts from a large adversarial perturbation and then seeks to reduce the perturbation while staying adversarial.The attack is conceptually simple, requires close to no hyperparameter tuning, does not rely on substitute models and is competitive with the best gradient-based attacks in standard computer vision tasks like ImageNet.We apply the attack on two black-box algorithms from Clarifai.com.The Boundary Attack in particular and the class of decision-based attacks in general open new avenues to study the robustness of machine learning models and raise new questions regarding the safety of deployed machine learning systems.An implementation of the attack is available as part of Foolbox (https://github.com/bethgelab/foolbox).", "target": ["Un nuovo adversarial attack che può attaccare direttamente i modelli di machine learning black-box del mondo reale senza transfer."]} +{"source": "We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a negligible cost in predictive accuracy. Our work builds on recent advances in the training of deep networks on user-partitioned data and privacy accounting for stochastic gradient descent.In particular, we add user-level privacy protection to the federated averaging algorithm, which makes large step updates from user-level data.Our work demonstrates that given a dataset with a sufficiently large number of users (a requirement easily met by even small internet-scale datasets), achieving differential privacy comes at the cost of increased computation, rather than in decreased utility as in most prior work.We find that our private LSTM language models are quantitatively and qualitatively similar to un-noised models when trained on a large dataset.", "target": ["La privacy differenziale a livello di utente per i language model delle reti neurali ricorrenti è possibile con un dataset sufficientemente grande."]} +{"source": "Convolutional neural networks (CNNs) are commonly trained using a fixed spatial image size predetermined for a given model.Although trained on images of a specific size, it is well established that CNNs can be used to evaluate a wide range of image sizes at test time, by adjusting the size of intermediate feature maps. In this work, we describe and evaluate a novel mixed-size training regime that mixes several image sizes at training time.We demonstrate that models trained using our method are more resilient to image size changes and generalize well even on small images.This allows faster inference by using smaller images at test time.For instance, we receive a 76.43% top-1 accuracy using ResNet50 with an image size of 160, which matches the accuracy of the baseline model with 2x fewer computations.Furthermore, for a given image size used at test time, we show this method can be exploited either to accelerate training or the final test accuracy.For example, we are able to reach a 79.27% accuracy with a model evaluated at a 288 spatial size for a relative improvement of 14% over the baseline.", "target": ["Addestrare le convnet con immagini di dimensioni miste può migliorare i risultati su più dimensioni nella fase di valutazione"]} +{"source": "We propose a simple technique for encouraging generative RNNs to plan ahead.We train a ``backward'' recurrent network to generate a given sequence in reverse order, and we encourage states of the forward model to predict cotemporal states of the backward model.The backward network is used only during training, and plays no role during sampling or inference.We hypothesize that our approach eases modeling of long-term dependencies by implicitly forcing the forward states to hold information about the longer-term future (as contained in the backward states).We show empirically that our approach achieves 9% relative improvement for a speech recognition task, and achieves significant improvement on a COCO caption generation task.", "target": ["L'articolo introduce un metodo di training delle reti ricorrenti generative che aiuta a pianificare in anticipo. Eseguiamo una seconda RNN in una direzione inversa e imponiamo un vincolo soft tra gli stati cotemporali forward e backward."]} +{"source": "Deep generative models seek to recover the process with which the observed data was generated.They may be used to synthesize new samples or to subsequently extract representations.Successful approaches in the domain of images are driven by several core inductive biases.However, a bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked.In this work we propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition.This provides a way to efficiently learn a more accurate generative model of real-world images, and serves as an initial step towards learning corresponding object representations.We evaluate our approach on several multi-object image datasets, and find that the generator learns to identify and disentangle information corresponding to different objects at a representational level.A human study reveals that the resulting generative model is better at generating images that are more faithful to the reference distribution.", "target": ["Proponiamo di strutturare il generatore di una GAN per considerare esplicitamente gli oggetti e le loro relazioni, e generare immagini per mezzo della composizione"]} +{"source": "Current literature in machine learning holds that unaligned, self-interested agents do not learn to use an emergent communication channel.We introduce a new sender-receiver game to study emergent communication for this spectrum of partially-competitive scenarios and put special care into evaluation.We find that communication can indeed emerge in partially-competitive scenarios, and we discover three things that are tied to improving it.First, that selfish communication is proportional to cooperation, and it naturally occurs for situations that are more cooperative than competitive.Second, that stability and performance are improved by using LOLA (Foerster et al, 2018), especially in more competitive scenarios.And third, that discrete protocols lend themselves better to learning cooperative communication than continuous ones.", "target": ["Riusciamo a far emergere la comunicazione con agenti egoisti, contrariamente alla visione corrente in ML"]} +{"source": "Deep neural networks (DNNs) typically have enough capacity to fit random data by brute force even when conventional data-dependent regularizations focusing on the geometry of the features are imposed.We find out that the reason for this is the inconsistency between the enforced geometry and the standard softmax cross entropy loss.To resolve this, we propose a new framework for data-dependent DNN regularization, the Geometrically-Regularized-Self-Validating neural Networks (GRSVNet).During training, the geometry enforced on one batch of features is simultaneously validated on a separate batch using a validation loss consistent with the geometry.We study a particular case of GRSVNet, the Orthogonal-Low-rank Embedding (OLE)-GRSVNet, which is capable of producing highly discriminative features residing in orthogonal low-rank subspaces.Numerical experiments show that OLE-GRSVNet outperforms DNNs with conventional regularization when trained on real data.More importantly, unlike conventional DNNs, OLE-GRSVNet refuses to memorize random data or random labels, suggesting it only learns intrinsic patterns by reducing the memorizing capacity of the baseline DNN.", "target": ["proponiamo un nuovo framework per la regolarizzazione di DNN dipendente dai dati che può impedire alle DNN di adattarsi troppo ai dati casuali o alle label casuali."]} +{"source": "End-to-end automatic speech recognition (ASR) commonly transcribes audio signals into sequences of characters while its performance is evaluated by measuring the word-error rate (WER).This suggests that predicting sequences of words directly may be helpful instead.However, training with word-level supervision can be more difficult due to the sparsity of examples per label class.In this paper we analyze an end-to-end ASR model that combines a word-and-character representation in a multi-task learning (MTL) framework.We show that it improves on the WER and study how the word-level model can benefit from character-level supervision by analyzing the learned inductive preference bias of each model component empirically.We find that by adding character-level supervision, the MTL model interpolates between recognizing more frequent words (preferred by the word-level model) and shorter words (preferred by the character-level model).", "target": ["L'apprendimento multi-task migliora il riconoscimento vocale a livello di parole e caratteri interpolando i bias di preferenza dei suoi componenti: la frequenza e la lunghezza delle parole."]} +{"source": "Discretizing floating-point vectors is a fundamental step of modern indexing methods.State-of-the-art techniques learn parameters of the quantizers on training data for optimal performance, thus adapting quantizers to the data.In this work, we propose to reverse this paradigm and adapt the data to the quantizer: we train a neural net whose last layers form a fixed parameter-free quantizer, such as pre-defined points of a sphere.As a proxy objective, we design and train a neural network that favors uniformity in the spherical latent space, while preserving the neighborhood structure after the mapping. For this purpose, we propose a new regularizer derived from the Kozachenko-Leonenko differential entropy estimator and combine it with a locality-aware triplet loss. Experiments show that our end-to-end approach outperforms most learned quantization methods, and is competitive with the state of the art on widely adopted benchmarks.Further more, we show that training without the quantization step results in almost no difference in accuracy, but yields a generic catalyser that can be applied with any subsequent quantization technique.", "target": ["Impariamo una rete neurale che rende uniforme la distribuzione dell'input, il che porta a prestazioni di indicizzazione competitive nello spazio ad alta dimensionalità"]} +{"source": "Temporal difference (TD) learning is a popular algorithm for policy evaluation in reinforcement learning, but the vanilla TD can substantially suffer from the inherent optimization variance.A variance reduced TD (VRTD) algorithm was proposed by Korda and La (2015), which applies the variance reduction technique directly to the online TD learning with Markovian samples.In this work, we first point out the technical errors in the analysis of VRTD in Korda and La (2015), and then provide a mathematically solid analysis of the non-asymptotic convergence of VRTD and its variance reduction performance.We show that VRTD is guaranteed to converge to a neighborhood of the fixed-point solution of TD at a linear convergence rate.Furthermore, the variance error (for both i.i.d. and Markovian sampling) and the bias error (for Markovian sampling) of VRTD are significantly reduced by the batch size of variance reduction in comparison to those of vanilla TD.", "target": ["Questo articolo fornisce uno studio rigoroso del TD learning a varianza ridotta e caratterizza il suo vantaggio rispetto al TD learning standard"]} +{"source": "We tackle unsupervised domain adaptation by accounting for the fact that different domains may need to be processed differently to arrive to a common feature representation effective for recognition.To this end, we introduce a deep learning framework where each domain undergoes a different sequence of operations, allowing some, possibly more complex, domains to go through more computations than others.This contrasts with state-of-the-art domain adaptation techniques that force all domains to be processed with the same series of operations, even when using multi-stream architectures whose parameters are not shared.As evidenced by our experiments, the greater flexibility of our method translates to higher accuracy.Furthermore, it allows us to handle any number of domains simultaneously.", "target": ["Una rete multiflow è un'architettura dinamica per domain adaptation che apprende grafi computazionali potenzialmente diversi per ogni dominio, in modo da mapparli in una rappresentazione comune in cui l'inferenza può essere eseguita in modo domain-agnostic."]} +{"source": "The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time.To accelerate training, practitioners often turn to distributed reinforcement learning architectures to parallelize and accelerate the training process.However, modern methods for scalable reinforcement learning (RL) often tradeoff between the throughput of samples that an RL agent can learn from (sample throughput) and the quality of learning from each sample (sample efficiency).In these scalable RL architectures, as one increases sample throughput (i.e. increasing parallelization in IMPALA (Espeholt et al., 2018)), sample efficiency drops significantly.To address this, we propose a new distributed reinforcement learning algorithm, IMPACT.IMPACT extends PPO with three changes: a target network for stabilizing the surrogate objective, a circular buffer, and truncated importance sampling.In discrete action-space environments, we show that IMPACT attains higher reward and, simultaneously, achieves up to 30% decrease in training wall-time than that of IMPALA.For continuous control environments, IMPACT trains faster than existing scalable agents while preserving the sample efficiency of synchronous PPO.", "target": ["IMPACT aiuta gli agenti RL ad addestrarsi più velocemente, diminuendo il wall-clock di training e aumentando contemporaneamente la sample efficiency."]} +{"source": "In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks (MPNNs).More specifically, we introduce a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate identical node attributes, and show that this representation is a universal approximator of continuous functions on graphs with node attributes.Our method relies on separability, a key topological characteristic that allows to extend well-chosen neural networks into universal representations.Finally, we show experimentally that CLIP is capable of capturing structural characteristics that traditional MPNNs fail to distinguish, while being state-of-the-art on benchmark graph classification datasets.", "target": ["Questo articolo introduce uno schema di colorazione per la disambiguazione dei nodi nelle reti neurali a grafo basato sulla separabilità, che si dimostra essere un'estensione MPNN universale."]} +{"source": "In this paper we present a method for algorithmic melody generation using a generative adversarial network without recurrent components.Music generation has been successfully done using recurrent neural networks, where the model learns sequence information that can help create authentic sounding melodies. Here, we use DCGAN architecture with dilated convolutions and towers to capture sequential information as spatial image information, and learn long-range dependencies in fixed-length melody forms such as Irish traditional reel.", "target": ["Rappresentando le melodie come immagini con unità semantiche allineate possiamo generarle usando una DCGAN senza componenti ricorrenti."]} +{"source": "Neural machine translation (NMT) systems have reached state of the art performance in translating text and widely deployed. Yet little is understood about how these systems function or break. Here we show that NMT systems are susceptible to producing highly pathological translations that are completely untethered from the source material, which we term hallucinations. Such pathological translations are problematic because they are are deeply disturbing of user trust and easy to find. We describe a method t generate hallucinations and show that many common variations of the NMT architecture are susceptible to them.We study a variety of approaches to reduce the frequency of hallucinations, including data augmentation, dynamical systems and regularization techniques and show that data augmentation significantly reduces hallucination frequency.Finally, we analyze networks that produce hallucinations and show signatures of hallucinations in the attention matrix and in the stability measures of the decoder.", "target": ["Introduciamo e analizziamo il fenomeno delle \"allucinazioni\" nella NMT, o traduzioni spurie non correlate al testo di origine, e proponiamo metodi per ridurne la frequenza."]} +{"source": "For AI systems to garner widespread public acceptance, we must develop methods capable of explaining the decisions of black-box models such as neural networks.In this work, we identify two issues of current explanatory methods.First, we show that two prevalent perspectives on explanations—feature-additivity and feature-selection—lead to fundamentally different instance-wise explanations.In the literature, explainers from different perspectives are currently being directly compared, despite their distinct explanation goals.The second issue is that current post-hoc explainers have only been thoroughly validated on simple models, such as linear regression, and, when applied to real-world neural networks, explainers are commonly evaluated under the assumption that the learned models behave reasonably.However, neural networks often rely on unreasonable correlations, even when producing correct decisions.We introduce a verification framework for explanatory methods under the feature-selection perspective.Our framework is based on a non-trivial neural network architecture trained on a real-world task, and for which we are able to provide guarantees on its inner workings.We validate the efficacy of our evaluation by showing the failure modes of current explainers.We aim for this framework to provide a publicly available,1 off-the-shelf evaluation when the feature-selection perspective on explanations is needed.", "target": ["Un framework di valutazione basato su una rete neurale del mondo reale per metodi esplicativi post-hoc"]} +{"source": "Planning in high-dimensional space remains a challenging problem, even with recent advances in algorithms and computational power.We are inspired by efference copy and sensory reafference theory from neuroscience. Our aim is to allow agents to form mental models of their environments for planning. The cerebellum is emulated with a two-stream, fully connected, predictor network.The network receives as inputs the efference as well as the features of the current state.Building on insights gained from knowledge distillation methods, we choose as our features the outputs of a pre-trained network, yielding a compressed representation of the current state. The representation is chosen such that it allows for fast search using classical graph search algorithms.We display the effectiveness of our approach on a viewpoint-matching task using a modified best-first search algorithm.", "target": ["Presentiamo un metodo ispirato alle neuroscienze basato su reti neurali per la ricerca nello spazio latente"]} +{"source": "Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks.Introducing the concept of an optimal representation space, we provide a simple theoretical resolution to this apparent paradox.In addition, we present a straightforward procedure that, without any retraining or architectural modifications, allows deep recurrent models to perform equally well (and sometimes better) when compared to shallow models.To validate our analysis, we conduct a set of consistent empirical evaluations and introduce several new sentence embedding models in the process.Even though this work is presented within the context of natural language processing, the insights are readily applicable to other domains that rely on distributed representations for transfer tasks.", "target": ["Introducendo la nozione di uno spazio di rappresentazione ottimale, forniamo un argomento teorico e una convalida sperimentale che un modello unsupervised per le frasi può funzionare bene sia in task di similarità supervisionata che di transfer unsupervised."]} +{"source": "Cloze test is widely adopted in language exams to evaluate students' language proficiency.In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH in which the questions were used in middle-school and high-school language exams.With the missing blanks carefully created by teachers and candidate choices purposely designed to be confusing, CLOTH requires a deeper language understanding and a wider attention span than previous automatically generated cloze datasets.We show humans outperform dedicated designed baseline models by a significant margin, even when the model is trained on sufficiently large external data.We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending a long-term context to be the key bottleneck.In addition, we find that human-designed data leads to a larger gap between the model's performance and human performance when compared to automatically generated data.", "target": ["Un test dataset di tipo cloze progettato dagli insegnanti per valutare le competenze linguistiche"]} +{"source": "Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain.While response properties of neurons in artificial neural networks bear similarities to those in the brain, the network architectures are often constrained to be different.Here we ask if a neural network can recover both neural representations and, if the architecture is unconstrained and optimized, also the anatomical properties of neural circuits.We demonstrate this in a system where the connectivity and the functional organization have been characterized, namely, the head direction circuit of the rodent and fruit fly.We trained recurrent neural networks (RNNs) to estimate head direction through integration of angular velocity.We found that the two distinct classes of neurons observed in the head direction system, the Ring neurons and the Shifter neurons, emerged naturally in artificial neural networks as a result of training.Furthermore, connectivity analysis and in-silico neurophysiology revealed structural and mechanistic similarities between artificial networks and the head direction system.Overall, our results show that optimization of RNNs in a goal-driven task can recapitulate the structure and function of biological circuits, suggesting that artificial neural networks can be used to study the brain at the level of both neural activity and anatomical organization.", "target": ["Le reti neurali artificiali addestrate con la gradient descent sono in grado di ricapitolare sia l'attività neurale realistica che l'organizzazione anatomica di un circuito biologico."]} +{"source": "Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices.Their energy is dominated by the number of multiplies needed to perform the convolutions.Winograd’s minimal filtering algorithm (Lavin, 2015) and network pruning (Han et al., 2015) can reduce the operation count, but these two methods cannot be straightforwardly combined — applying the Winograd transform fills in the sparsity in both the weights and the activations.We propose two modifications to Winograd-based CNNs to enable these methods to exploit sparsity.First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations.Second, we prune the weights in the Winograd domain to exploit static weight sparsity.For models on CIFAR-10, CIFAR-100 and ImageNet datasets, our method reduces the number of multiplications by 10.4x, 6.8x and 10.8x respectively with loss of accuracy less than 0.1%, outperforming previous baselines by 2.0x-3.0x.We also show that moving ReLU to the Winograd domain allows more aggressive pruning.", "target": ["Prune e ReLU nel dominio di Winograd per una rete neurale convoluzionale efficiente"]} +{"source": "In this paper we present a novel optimization algorithm called Advanced Neuroevolution.The aim for this algorithm is to train deep neural networks, and eventually act as an alternative to Stochastic Gradient Descent (SGD) and its variants as needed.We evaluated our algorithm on the MNIST dataset, as well as on several global optimization problems such as the Ackley function.We find the algorithm performing relatively well for both cases, overtaking other global optimization algorithms such as Particle Swarm Optimization (PSO) and Evolution Strategies (ES).", "target": ["Un nuovo algoritmo per addestrare deep neural network. Testato su funzioni di ottimizzazione e MNIST."]} +{"source": "Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies.Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limiting the variance reduction effect of large mini-batches.We introduce flipout, an efficient method for decorrelating the gradients within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example.Empirically, flipout achieves the ideal linear variance reduction for fully connected networks, convolutional networks, and RNNs.We find significant speedups in training neural networks with multiplicative Gaussian perturbations.We show that flipout is effective at regularizing LSTMs, and outperforms previous methods.Flipout also enables us to vectorize evolution strategies: in our experiments, a single GPU with flipout can handle the same throughput as at least 40 CPU cores using existing methods, equivalent to a factor-of-4 cost reduction on Amazon Web Services.", "target": ["Introduciamo il flipout, un metodo efficiente per decorrelare i gradienti calcolati dai pesi delle reti neurali stocastiche all'interno di un mini-batch, campionando implicitamente perturbazioni di peso pseudo-indipendenti per ogni esempio."]} +{"source": "Deep generative models such as Variational AutoEncoder (VAE) and Generative Adversarial Network (GAN) play an increasingly important role in machine learning and computer vision.However, there are two fundamental issues hindering their real-world applications: the difficulty of conducting variational inference in VAE and the functional absence of encoding real-world samples in GAN.In this paper, we propose a novel algorithm named Latently Invertible Autoencoder (LIA) to address the above two issues in one framework.An invertible network and its inverse mapping are symmetrically embedded in the latent space of VAE.Thus the partial encoder first transforms the input into feature vectors and then the distribution of these feature vectors is reshaped to fit a prior by the invertible network.The decoder proceeds in the reverse order of the encoder's composite mappings.A two-stage stochasticity-free training scheme is designed to train LIA via adversarial learning, in the sense that the decoder of LIA is first trained as a standard GAN with the invertible network and then the partial encoder is learned from an autoencoder by detaching the invertible network from LIA. Experiments conducted on the FFHQ face dataset and three LSUN datasets validate the effectiveness of LIA for inference and generation.", "target": ["Un nuovo modello Latently Invertible Autoencoder è proposto per risolvere il problema dell'inferenza variazionale in VAE utilizzando la rete invertibile e il training adversarial a due stadi."]} +{"source": "Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism.Despite the potential to increase the interpretability and the compositionality of the behavior of artificial agents, it remains difficult to learn from demonstrations neural networks that represent computer programs.The main challenges that set algorithmic domains apart from other imitation learning domains are the need for high accuracy, the involvement of specific structures of data, and the extremely limited observability.To address these challenges, we propose to model programs as Parametrized Hierarchical Procedures (PHPs).A PHP is a sequence of conditional operations, using a program counter along with the observation to select between taking an elementary action, invoking another PHP as a sub-procedure, and returning to the caller.We develop an algorithm for training PHPs from a set of supervisor demonstrations, only some of which are annotated with the internal call structure, and apply it to efficient level-wise training of multi-level PHPs.We show in two benchmarks, NanoCraft and long-hand addition, that PHPs can learn neural programs more accurately from smaller amounts of both annotated and unannotated demonstrations.", "target": ["Introduciamo il modello PHP per la rappresentazione gerarchica dei programmi neurali, e un algoritmo per l'apprendimento di PHP da una miscela di supervisione forte e debole."]} +{"source": "Deep Reinforcement Learning has managed to achieve state-of-the-art results in learning control policies directly from raw pixels.However, despite its remarkable success, it fails to generalize, a fundamental component required in a stable Artificial Intelligence system.Using the Atari game Breakout, we demonstrate the difficulty of a trained agent in adjusting to simple modifications in the raw image, ones that a human could adapt to trivially.In transfer learning, the goal is to use the knowledge gained from the source task to make the training of the target task faster and better.We show that using various forms of fine-tuning, a common method for transfer learning, is not effective for adapting to such small visual changes.In fact, it is often easier to re-train the agent from scratch than to fine-tune a trained agent.We suggest that in some cases transfer learning can be improved by adding a dedicated component whose goal is to learn to visually map between the known domain and the new one.Concretely, we use Unaligned Generative Adversarial Networks (GANs) to create a mapping function to translate images in the target task to corresponding images in the source task.These mapping functions allow us to transform between various variations of the Breakout game, as well as between different levels of a Nintendo game, Road Fighter.We show that learning this mapping is substantially more efficient than re-training.A visualization of a trained agent playing Breakout and Road Fighter, with and without the GAN transfer, can be seen in \\url{https://streamable.com/msgtm} and \\url{https://streamable.com/5e2ka}.", "target": ["Proponiamo un metodo per trasferire la conoscenza tra task RL correlati usando mappature visive, e dimostriamo la sua efficacia su varianti visive del gioco Atari Breakout e diversi livelli di Road Fighter, un gioco di guida automobilistica Nintendo."]} +{"source": "A commonplace belief in the machine learning community is that using adaptive gradient methods hurts generalization.We re-examine this belief both theoretically and experimentally, in light of insights and trends from recent years.We revisit some previous oft-cited experiments and theoretical accounts in more depth, and provide a new set of experiments in larger-scale, state-of-the-art settings.We conclude that with proper tuning, the improved training performance of adaptive optimizers does not in general carry an overfitting penalty, especially in contemporary deep learning.Finally, we synthesize a ``user's guide'' to adaptive optimizers, including some proposed modifications to AdaGrad to mitigate some of its empirical shortcomings.", "target": ["I metodi a gradiente adattivo, se ben applicati, non incorrono in una penalità di generalizzazione."]} +{"source": "The ability to generalize quickly from few observations is crucial for intelligent systems.In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered.These past observations are recalled from an external memory module and processed by a decoder network that can combine information from different memory slots to generalize beyond direct recall.We show this algorithm can perform as well as state of the art baselines on few-shot classification benchmarks with a smaller memory footprint. In addition, its memory compression allows it to scale to thousands of unknown labels. Finally, we introduce a meta-learning reasoning task which is more challenging than direct classification.In this setting, APL is able to generalize with fewer than one example per class via deductive reasoning.", "target": ["Introduciamo un modello che generalizza rapidamente da poche osservazioni memorizzando informazioni sorprendenti e frequentando i dati più rilevanti ad ogni punto temporale."]} +{"source": "Recent advances in recurrent neural nets (RNNs) have shown much promise in many applications in natural language processing.For most of these tasks, such as sentiment analysis of customer reviews, a recurrent neural net model parses the entire review before forming a decision.We argue that reading the entire input is not always necessary in practice, since a lot of reviews are often easy to classify, i.e., a decision can be formed after reading some crucial sentences or words in the provided text.In this paper, we present an approach of fast reading for text classification.Inspired by several well-known human reading techniques, our approach implements an intelligent recurrent agent which evaluates the importance of the current snippet in order to decide whether to make a prediction, or to skip some texts, or to re-read part of the sentence.Our agent uses an RNN module to encode information from the past and the current tokens, and applies a policy module to form decisions.With an end-to-end training algorithm based on policy gradient, we train and test our agent on several text classification datasets and achieve both higher efficiency and better accuracy compared to previous approaches.", "target": ["Sviluppiamo un approccio addestrabile end-to-end per lo skimming, il rereading e l'early stopping applicabile ai task di classificazione."]} +{"source": "Reinforcement learning agents need to explore their unknown environments to solve the tasks given to them.The Bayes optimal solution to exploration is intractable for complex environments, and while several exploration methods have been proposed as approximations, it remains unclear what underlying objective is being optimized by existing exploration methods, or how they can be altered to incorporate prior knowledge about the task.Moreover, it is unclear how to acquire a single exploration strategy that will be useful for solving multiple downstream tasks.We address these shortcomings by learning a single exploration policy that can quickly solve a suite of downstream tasks in a multi-task setting, amortizing the cost of learning to explore.We recast exploration as a problem of State Marginal Matching (SMM), where we aim to learn a policy for which the state marginal distribution matches a given target state distribution, which can incorporate prior knowledge about the task.We optimize the objective by reducing it to a two-player, zero-sum game between a state density model and a parametric policy.Our theoretical analysis of this approach suggests that prior exploration methods do not learn a policy that does distribution matching, but acquire a replay buffer that performs distribution matching, an observation that potentially explains these prior methods' success in single-task settings.On both simulated and real-world tasks, we demonstrate that our algorithm explores faster and adapts more quickly than prior methods.", "target": ["Vediamo l'esplorazione in RL come un problema di corrispondenza di una distribuzione marginale sugli stati."]} +{"source": "The effectiveness of Convolutional Neural Networks stems in large part from their ability to exploit the translation invariance that is inherent in many learning problems.Recently, it was shown that CNNs can exploit other invariances, such as rotation invariance, by using group convolutions instead of planar convolutions.However, for reasons of performance and ease of implementation, it has been necessary to limit the group convolution to transformations that can be applied to the filters without interpolation.Thus, for images with square pixels, only integer translations, rotations by multiples of 90 degrees, and reflections are admissible.Whereas the square tiling provides a 4-fold rotational symmetry, a hexagonal tiling of the plane has a 6-fold rotational symmetry.In this paper we show how one can efficiently implement planar convolution and group convolution over hexagonal lattices, by re-using existing highly optimized convolution routines.We find that, due to the reduced anisotropy of hexagonal filters, planar HexaConv provides better accuracy than planar convolution with square filters, given a fixed parameter budget.Furthermore, we find that the increased degree of symmetry of the hexagonal grid increases the effectiveness of group convolutions, by allowing for more parameter sharing.We show that our method significantly outperforms conventional CNNs on the AID aerial scene classification dataset, even outperforming ImageNet pre-trained models.", "target": ["Introduciamo G-HexaConv, una rete neurale convoluzionale equivariante di gruppo su lattici esagonali."]} +{"source": "Deep Convolutional Neural Networks (CNNs) have been repeatedly shown to perform well on image classification tasks, successfully recognizing a broad array of objects when given sufficient training data.Methods for object localization, however, are still in need of substantial improvement.Common approaches to this problem involve the use of a sliding window, sometimes at multiple scales, providing input to a deep CNN trained to classify the contents of the window.In general, these approaches are time consuming, requiring many classification calculations.In this paper, we offer a fundamentally different approach to the localization of recognized objects in images.Our method is predicated on the idea that a deep CNN capable of recognizing an object must implicitly contain knowledge about object location in its connection weights.We provide a simple method to interpret classifier weights in the context of individual classified images.This method involves the calculation of the derivative of network generated activation patterns, such as the activation of output class label units, with regard to each in- put pixel, performing a sensitivity analysis that identifies the pixels that, in a local sense, have the greatest influence on internal representations and object recognition.These derivatives can be efficiently computed using a single backward pass through the deep CNN classifier, producing a sensitivity map of the image.We demonstrate that a simple linear mapping can be learned from sensitivity maps to bounding box coordinates, localizing the recognized object.Our experimental results, using real-world data sets for which ground truth localization information is known, reveal competitive accuracy from our fast technique.", "target": ["Proporre un nuovo approccio di localizzazione degli oggetti basato sull'interpretazione della CNN profonda utilizzando la rappresentazione interna e i pensieri della rete"]} +{"source": "We present trellis networks, a new architecture for sequence modeling.On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers.On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices.Thus trellis networks with general weight matrices generalize truncated recurrent networks.We leverage these connections to design high-performing trellis networks that absorb structural and algorithmic elements from both recurrent and convolutional models.Experiments demonstrate that trellis networks outperform the current state of the art methods on a variety of challenging benchmarks, including word-level language modeling and character-level language modeling tasks, and stress tests designed to evaluate long-term memory retention.The code is available at https://github.com/locuslab/trellisnet .", "target": ["Le reti Trellis sono una nuova architettura di modellazione di sequenze che collega i modelli ricorrenti e convoluzionali e stabilisce un nuovo stato dell'arte nel language modelling a livello di parole e caratteri."]} +{"source": "We propose an end-to-end framework for training domain specific models (DSMs) to obtain both high accuracy and computational efficiency for object detection tasks.DSMs are trained with distillation and focus on achieving high accuracy at a limited domain (e.g. fixed view of an intersection).We argue that DSMs can capture essential features well even with a small model size, enabling higher accuracy and efficiency than traditional techniques. In addition, we improve the training efficiency by reducing the dataset size by culling easy to classify images from the training set.For the limited domain, we observed that compact DSMs significantly surpass the accuracy of COCO trained models of the same size.By training on a compact dataset, we show that with an accuracy drop of only 3.6%, the training time can be reduced by 93%.", "target": ["Si può ottenere un'elevata accuratezza di rilevamento degli oggetti addestrando modelli compatti specifici del dominio e il training può essere molto breve."]} +{"source": "We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, $Q$-functions, and dynamics. We show, theoretically and empirically, that even for one-dimensional continuous state space, there are many MDPs whose optimal $Q$-functions and policies are much more complex than the dynamics.We hypothesize many real-world MDPs also have a similar property.For these MDPs, model-based planning is a favorable algorithm, because the resulting policies can approximate the optimal policy significantly better than a neural network parameterization can, and model-free or model-based policy optimization rely on policy parameterization.Motivated by the theory, we apply a simple multi-step model-based bootstrapping planner (BOOTS) to bootstrap a weak $Q$-function into a stronger policy.Empirical results show that applying BOOTS on top of model-based or model-free policy optimization algorithms at the test time improves the performance on MuJoCo benchmark tasks.", "target": ["Confrontiamo algoritmi di RL profondi basati su modelli e senza modelli studiando l'approssimabilità delle funzioni $Q$, delle policy e delle dinamiche da parte delle reti neurali."]} +{"source": "Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world.For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved.In order to match real-world conditions this causal knowledge must be learned without access to supervised data.To address this problem we present a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion.It incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently.On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches that do not incorporate such prior knowledge.We demonstrate its ability to handle occlusion and show that it can extrapolate learned knowledge to scenes with different numbers of objects.", "target": ["Introduciamo un nuovo approccio al ragionamento fisico di senso comune che impara a scoprire gli oggetti e a modellare le loro interazioni fisiche da immagini visive grezze in modo puramente unsupervised"]} +{"source": "The idea that neural networks may exhibit a bias towards simplicity has a long history.Simplicity bias provides a way to quantify this intuition. It predicts, for a broad class of input-output maps which can describe many systems in science and engineering, that simple outputs are exponentially more likely to occur upon uniform random sampling of inputs than complex outputs are. This simplicity bias behaviour has been observed for systems ranging from the RNA sequence to secondary structure map, to systems of coupled differential equations, to models of plant growth. Deep neural networks can be viewed as a mapping from the space of parameters (the weights) to the space of functions (how inputs get transformed to outputs by the network). We show that this parameter-function map obeys the necessary conditions for simplicity bias, and numerically show that it is hugely biased towards functions with low descriptional complexity. We also demonstrate a Zipf like power-law probability-rank relation. A bias towards simplicity may help explain why neural nets generalize so well.", "target": ["In molte mappe input-output semplici si osserva una distorsione molto forte verso gli output semplici. La mappa parametro-funzione delle deep network risulta essere biased nello stesso modo."]} +{"source": "Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior.However, directing IL to achieve arbitrary goals is difficult.In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals.Yet, reward functions that evoke desirable behavior are often difficult to specify.In this paper, we propose \"Imitative Models\" to combine the benefits of IL and goal-directed planning.Imitative Models are probabilistic predictive models of desirable behavior able to plan interpretable expert-like trajectories to achieve specified goals.We derive families of flexible goal objectives, including constrained goal regions, unconstrained goal sets, and energy-based goals.We show that our method can use these objectives to successfully direct behavior.Our method substantially outperforms six IL approaches and a planning-based approach in a dynamic simulated autonomous driving task, and is efficiently learned from expert demonstrations without online data collection. We also show our approach is robust to poorly-specified goals, such as goals on the wrong side of the road.", "target": ["In questo articolo, proponiamo modelli imitativi per combinare i vantaggi dell'IL e del goal-direct planning: modelli predittivi probabilistici di comportamenti desiderabili in grado di pianificare traiettorie interpretabili simili a quelle degli esperti per raggiungere obiettivi specifici."]} +{"source": "Learning communication via deep reinforcement learning has recently been shown to be an effective way to solve cooperative multi-agent tasks.However, learning which communicated information is beneficial for each agent's decision-making remains a challenging task.In order to address this problem, we introduce a fully differentiable framework for communication and reasoning, enabling agents to solve cooperative tasks in partially-observable environments.The framework is designed to facilitate explicit reasoning between agents, through a novel memory-based attention network that can learn selectively from its past memories.The model communicates through a series of reasoning steps that decompose each agent's intentions into learned representations that are used first to compute the relevance of communicated information, and second to extract information from memories given newly received information.By selectively interacting with new information, the model effectively learns a communication protocol directly, in an end-to-end manner.We empirically demonstrate the strength of our model in cooperative multi-agent tasks, where inter-agent communication and reasoning over prior information substantially improves performance compared to baselines.", "target": ["Nuova architettura del meccanismo di attention basato sulla memoria per la comunicazione multi-agente."]} +{"source": "In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system from observed state trajectories.To achieve better generalization with fewer training samples, SymODEN incorporates appropriate inductive bias by designing the associated computation graph in a physics-informed manner.In particular, we enforce Hamiltonian dynamics with control to learn the underlying dynamics in a transparent way which can then be leveraged to draw insight about relevant physical aspects of the system, such as mass and potential energy.In addition, we propose a parametrization which can enforce this Hamiltonian formalism even when the generalized coordinate data is embedded in a high-dimensional space or we can only access velocity data instead of generalized momentum.This framework, by offering interpretable, physically-consistent models for physical systems, opens up new possibilities for synthesizing model-based control strategies.", "target": ["Questo lavoro applica la dinamica hamiltoniana con il controllo per imparare i modelli di sistema dai dati embedded di posizione e velocità, e sfrutta questa dinamica fisicamente coerente per sintetizzare il controllo basato sul modello tramite la modellazione dell'energia."]} +{"source": "Federated learning, where a global model is trained by iterative parameter averaging of locally-computed updates, is a promising approach for distributed training of deep networks; it provides high communication-efficiency and privacy-preservability, which allows to fit well into decentralized data environments, e.g., mobile-cloud ecosystems.However, despite the advantages, the federated learning-based methods still have a challenge in dealing with non-IID training data of local devices (i.e., learners).In this regard, we study the effects of a variety of hyperparametric conditions under the non-IID environments, to answer important concerns in practical implementations:(i) We first investigate parameter divergence of local updates to explain performance degradation from non-IID data.The origin of the parameter divergence is also found both empirically and theoretically.(ii) We then revisit the effects of optimizers, network depth/width, and regularization techniques; our observations show that the well-known advantages of the hyperparameter optimization strategies could rather yield diminishing returns with non-IID data.(iii) We finally provide the reasons of the failure cases in a categorized way, mainly based on metrics of the parameter divergence.", "target": ["Indaghiamo le ragioni interne delle nostre osservazioni, gli effetti decrescenti dei ben noti metodi di ottimizzazione degli iperparametri sul federated learning dai dati non IID decentralizzati."]} +{"source": "Deep learning models are known to be vulnerable to adversarial examples.A practical adversarial attack should require as little as possible knowledge of attacked models T. Current substitute attacks need pre-trained models to generate adversarial examples and their attack success rates heavily rely on the transferability of adversarial examples.Current score-based and decision-based attacks require lots of queries for the T. In this study, we propose a novel adversarial imitation attack.First, it produces a replica of the T by a two-player game like the generative adversarial networks (GANs).The objective of the generative model G is to generate examples which lead D returning different outputs with T. The objective of the discriminative model D is to output the same labels with T under the same inputs.Then, the adversarial examples generated by D are utilized to fool the T. Compared with the current substitute attacks, imitation attack can use less training data to produce a replica of T and improve the transferability of adversarial examples.Experiments demonstrate that our imitation attack requires less training data than the black-box substitute attacks, but achieves an attack success rate close to the white-box attack on unseen data with no query.", "target": ["Un nuovo attacco di adversarial imitation per ingannare i modelli di apprendimento automatico."]} +{"source": "Stochastic Gradient Descent (SGD) methods using randomly selected batches are widely-used to train neural network (NN) models.Performing design exploration to find the best NN for a particular task often requires extensive training with different models on a large dataset, which is very computationally expensive.The most straightforward method to accelerate this computation is to distribute the batch of SGD over multiple processors.However, large batch training often times leads to degradation in accuracy, poor generalization, and even poor robustness to adversarial attacks. Existing solutions for large batch training either do not work or require massive hyper-parameter tuning.To address this issue, we propose a novel large batch training method which combines recent results in adversarial training (to regularize against ``sharp minima'') and second order optimization (to use curvature information to change batch size adaptively during training).We extensively evaluate our method on Cifar-10/100, SVHN, TinyImageNet, and ImageNet datasets, using multiple NNs, including residual networks as well as compressed networks such as SqueezeNext. Our new approach exceeds the performance of the existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\\% and $3\\times$, respectively).We emphasize that this is achieved without any additional hyper-parameter tuning to tailor our method to any of these experiments.", "target": ["Addestramento di grandi batch utilizzando adversarial training e le informazioni del secondo ordine"]} +{"source": "Inspired by neurophysiological discoveries of navigation cells in the mammalianbrain, we introduce the first deep neural network architecture for modeling EgocentricSpatial Memory (ESM).It learns to estimate the pose of the agent andprogressively construct top-down 2D global maps from egocentric views in a spatiallyextended environment.During the exploration, our proposed ESM networkmodel updates belief of the global map based on local observations using a recurrentneural network.It also augments the local mapping with a novel externalmemory to encode and store latent representations of the visited places based ontheir corresponding locations in the egocentric coordinate.This enables the agentsto perform loop closure and mapping correction.This work contributes in thefollowing aspects: first, our proposed ESM network provides an accurate mappingability which is vitally important for embodied agents to navigate to goal locations.In the experiments, we demonstrate the functionalities of the ESM network inrandom walks in complicated 3D mazes by comparing with several competitivebaselines and state-of-the-art Simultaneous Localization and Mapping (SLAM)algorithms.Secondly, we faithfully hypothesize the functionality and the workingmechanism of navigation cells in the brain.Comprehensive analysis of our modelsuggests the essential role of individual modules in our proposed architecture anddemonstrates efficiency of communications among these modules.We hope thiswork would advance research in the collaboration and communications over bothfields of computer science and computational neuroscience.", "target": ["La prima deep neural network per la modellazione della memoria spaziale egocentrica ispirata dalle scoperte neurofisiologiche delle cellule di navigazione nel cervello dei mammiferi"]} +{"source": "We show that if the usual training loss is augmented by a Lipschitz regularization term, then the networks generalize. We prove generalization by first establishing a stronger convergence result, along with a rate of convergence. A second result resolves a question posed in Zhang et al. (2016): how can a model distinguish between the case of clean labels, and randomized labels? Our answer is that Lipschitz regularization using the Lipschitz constant of the clean data makes this distinction. In this case, the model learns a different function which we hypothesize correctly fails to learn the dirty labels.", "target": ["Dimostriamo la generalizzazione delle DNN aggiungendo un termine di regolarizzazione Lipschitz alla loss di training. Risolviamo una questione posta in Zhang et al. (2016)."]} +{"source": "For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase when this requirement is imposed.Here, we report large improvements in error rates on multiple datasets, for deep convolutional neural networks deployed with 1-bit-per-weight.Using wide residual networks as our main baseline, our approach simplifies existing methods that binarize weights by applying the sign function in training; we apply scaling factors for each layer with constant unlearned values equal to the layer-specific standard deviations used for initialization.For CIFAR-10, CIFAR-100 and ImageNet, and models with 1-bit-per-weight requiring less than 10 MB of parameter memory, we achieve error rates of 3.9%, 18.5% and 26.0% / 8.5% (Top-1 / Top-5) respectively.We also considered MNIST, SVHN and ImageNet32, achieving 1-bit-per-weight test results of 0.27%, 1.9%, and 41.3% / 19.1% respectively.For CIFAR, our error rates halve previously reported values, and are within about 1% of our error-rates for the same network with full-precision weights.For networks that overfit, we also show significant improvements in error rate by not learning batch normalization scale and offset parameters.This applies to both full precision and 1-bit-per-weight networks.Using a warm-restart learning-rate schedule, we found that training for 1-bit-per-weight is just as fast as full-precision networks, with better accuracy than standard schedules, and achieved about 98%-99% of peak performance in just 62 training epochs for CIFAR-10/100.For full training code and trained models in MATLAB, Keras and PyTorch see https://github.com/McDonnell-Lab/1-bit-per-weight/ .", "target": ["Addestriamo ampie reti con residual connection che possono essere immediatamente distribuite usando solo un singolo bit per ogni peso convoluzionale, con una precisione significativamente migliore rispetto ai metodi passati."]} +{"source": "This paper presents a system for immersive visualization of Non-Euclidean spaces using real-time ray tracing.It exploits the capabilities of the new generation of GPU’s based on the NVIDIA’s Turing architecture in order to develop new methods for intuitive exploration of landscapes featuring non-trivial geometry and topology in virtual reality.", "target": ["Visualizzazione immersiva degli spazi non euclidei classici usando il Ray Tracing in tempo reale."]} +{"source": "We propose the set autoencoder, a model for unsupervised representation learning for sets of elements.It is closely related to sequence-to-sequence models, which learn fixed-sized latent representations for sequences, and have been applied to a number of challenging supervised sequence tasks such as machine translation, as well as unsupervised representation learning for sequences.In contrast to sequences, sets are permutation invariant.The proposed set autoencoder considers this fact, both with respect to the input as well as the output of the model.On the input side, we adapt a recently-introduced recurrent neural architecture using a content-based attention mechanism.On the output side, we use a stable marriage algorithm to align predictions to labels in the learning phase.We train the model on synthetic data sets of point clouds and show that the learned representations change smoothly with translations in the inputs, preserve distances in the inputs, and that the set size is represented directly.We apply the model to supervised tasks on the point clouds using the fixed-size latent representation.For a number of difficult classification problems, the results are better than those of a model that does not consider the permutation invariance.Especially for small training sets, the set-aware model benefits from unsupervised pretraining.", "target": ["Proponiamo il set autoencoder, un modello per l'apprendimento unsupervised della rappresentazione di insiemi di elementi."]} +{"source": "Deep reinforcement learning algorithms can learn complex behavioral skills, but real-world application of these methods requires a considerable amount of experience to be collected by the agent.In practical settings, such as robotics, this involves repeatedly attempting a task, resetting the environment between each attempt.However, not all tasks are easily or automatically reversible.In practice, this learning process requires considerable human intervention.In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and backward policy, with the backward policy resetting the environment for a subsequent attempt.By learning a value function for the backward policy, we can automatically determine when the forward policy is about to enter a non-reversible state, providing for uncertainty-aware safety aborts.Our experiments illustrate that proper use of the backward policy can greatly reduce the number of manual resets required to learn a task and can reduce the number of unsafe actions that lead to non-reversible states.", "target": ["Proponiamo un metodo autonomo per un reinforcement learning sicuro ed efficiente che impara simultaneamente una policy forward e una backward, con la policy backward che resetta l'ambiente per un tentativo successivo."]} +{"source": "It has been argued that current machine learning models do not have commonsense, and therefore must be hard-coded with prior knowledge (Marcus, 2018).Here we show surprising evidence that language models can already learn to capture certain common sense knowledge.Our key observation is that a language model can compute the probability of any statement, and this probability can be used to evaluate the truthfulness of that statement. On the Winograd Schema Challenge (Levesque et al., 2011), language models are 11% higher in accuracy than previous state-of-the-art supervised methods.Language models can also be fine-tuned for the task of Mining Commonsense Knowledge on ConceptNet to achieve an F1 score of 0.912 and 0.824, outperforming previous best results (Jastrzebskiet al., 2018). Further analysis demonstrates that language models can discover unique features of Winograd Schema contexts that decide the correct answers without explicit supervision.", "target": ["Presentiamo la prova che le LM catturano il senso comune con risultati allo stato dell'arte sia su Winograd Schema Challenge che su Commonsense Knowledge Mining."]} +{"source": "In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget.In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time.The suggested method acquires features incrementally based on a context-aware feature-value function.We formulate the problem in the reinforcement learning paradigm, and introduce a reward function based on the utility of each feature.Specifically, MC dropout sampling is used to measure expected variations of the model uncertainty which is used as a feature-value function.Furthermore, we suggest sharing representations between the class predictor and value function estimator networks.The suggested approach is completely online and is readily applicable to stream learning setups.The solution is evaluated on three different datasets including the well-known MNIST dataset as a benchmark as well as two cost-sensitive datasets: Yahoo Learning to Rank and a dataset in the medical domain for diabetes classification.According to the results, the proposed method is able to efficiently acquire features and make accurate predictions.", "target": ["Un algoritmo online per l'acquisizione e la predizione di feature consapevoli dei costi"]} +{"source": "This paper revisits the problem of sequence modeling using convolutional architectures. Although both convolutional and recurrent architectures havealong history in sequence prediction, the current \"default\" mindset in much ofthe deep learning community is that generic sequence modeling is best handledusing recurrent networks. The goal of this paper is to question this assumption. Specifically, we consider a simple generic temporal convolution network (TCN),which adopts features from modern ConvNet architectures such as a dilations and residual connections. We show that on a variety of sequence modeling tasks,including many frequently used as benchmarks for evaluating recurrent networks,the TCN outperforms baseline RNN methods (LSTMs, GRUs, and vanilla RNNs) andsometimes even highly specialized approaches. We further show that thepotential \"infinite memory\" advantage that RNNs have over TCNs is largelyabsent in practice: TCNs indeed exhibit longer effective history sizes than their recurrent counterparts. As a whole, we argue that it may be time to (re)consider ConvNets as the default \"go to\" architecture for sequence modeling.", "target": ["Noi sosteniamo che le reti convoluzionali dovrebbero essere considerate il punto di partenza predefinito per i task di modellazione delle sequenze."]} +{"source": "Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods.At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training.In many cases, it is possible to decompose a task to a series of functions, of which for some we may prefer to use a neural network to learn the functionality, while for others the preferred method would be to use existing black-box functions.We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions.We do so by approximating the black-box functionality with a differentiable neural network in a way that drives the base network to comply with the black-box function interface during the end-to-end optimization process.At inference time, we replace the differentiable estimator with its external black-box non-differentiable counterpart such that the base network output matches the input arguments of the black-box function.Using this ``Estimate and Replace'' paradigm, we train a neural network, end to end, to compute the input to black-box functionality while eliminating the need for intermediate labels.We show that by leveraging the existing precise black-box function during inference, the integrated model generalizes better than a fully differentiable model, and learns more efficiently compared to RL-based methods.", "target": ["Addestrare le DNN per interfacciarsi con le funzioni black box senza le label intermedie utilizzando una sottorete di stima che può essere sostituita con la black box dopo il training"]} +{"source": "This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation, which can be viewed as a two-player game between the actor and a critic-like function, which is named as dual critic. Compared to its actor-critic relatives, Dual-AC has the desired property that the actor and dual critic are updated cooperatively to optimize the same objective function, providing a more transparent way for learning the critic that is directly related to the objective function of the actor.We then provide a concrete algorithm that can effectively solve the minimax optimization problem, using techniques of multi-step bootstrapping, path regularization, and stochastic dual ascent algorithm.We demonstrate that the proposed algorithm achieves the state-of-the-art performances across several benchmarks.", "target": ["Proponiamo l'algoritmo Dual Actor-Critic, che è derivato dalla forma duale lagrangiana dell'equazione di ottimalità di Bellman. L'algoritmo raggiunge lo stato dell'arte delle prestazioni su diversi benchmark."]} +{"source": "Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied.However, it is often the case that data are abundant in some domains but scarce in others.Domain adaptation deals with the challenge of adapting a model trained from a data-rich source domain to perform well in a data-poor target domain.In general, this requires learning plausible mappings between domains.CycleGAN is a powerful framework that efficiently learns to map inputs from one domain to another using adversarial training and a cycle-consistency constraint.However, the conventional approach of enforcing cycle-consistency via reconstruction may be overly restrictive in cases where one or more domains have limited training data.In this paper, we propose an augmented cyclic adversarial learning model that enforces the cycle-consistency constraint via an external task specific model, which encourages the preservation of task-relevant content as opposed to exact reconstruction.This task specific model both relaxes the cycle-consistency constraint and complements the role of the discriminator during training, serving as an augmented information source for learning the mapping.We explore adaptation in speech and visual domains in low resource in supervised setting.In speech domains, we adopt a speech recognition model from each domain as the task specific model.Our approach improves absolute performance of speech recognition by 2% for female speakers in the TIMIT dataset, where the majority of training samples are from male voices.In low-resource visual domain adaptation, the results show that our approach improves absolute performance by 14% and 4% when adapting SVHN to MNIST and vice versa, respectively, which outperforms unsupervised domain adaptation methods that require high-resource unlabeled target domain.", "target": ["Un robusto domain adaptation impiegando una loss specifica per il task nel cyclic adversarial learning"]} +{"source": "The success of popular algorithms for deep reinforcement learning, such as policy-gradients and Q-learning, relies heavily on the availability of an informative reward signal at each timestep of the sequential decision-making process.When rewards are only sparsely available during an episode, or a rewarding feedback is provided only after episode termination, these algorithms perform sub-optimally due to the difficultly in credit assignment.Alternatively, trajectory-based policy optimization methods, such as cross-entropy method and evolution strategies, do not require per-timestep rewards, but have been found to suffer from high sample complexity by completing forgoing the temporal nature of the problem.Improving the efficiency of RL algorithms in real-world problems with sparse or episodic rewards is therefore a pressing need.In this work, we introduce a self-imitation learning algorithm that exploits and explores well in the sparse and episodic reward settings.We view each policy as a state-action visitation distribution and formulate policy optimization as a divergence minimization problem.We show that with Jensen-Shannon divergence, this divergence minimization problem can be reduced into a policy-gradient algorithm with shaped rewards learned from experience replays.Experimental results indicate that our algorithm works comparable to existing algorithms in environments with dense rewards, and significantly better in environments with sparse and episodic rewards.We then discuss limitations of self-imitation learning, and propose to solve them by using Stein variational policy gradient descent with the Jensen-Shannon kernel to learn multiple diverse policies.We demonstrate its effectiveness on a challenging variant of continuous-control MuJoCo locomotion tasks.", "target": ["Ottimizzazione della policy utilizzando i buoni rollout passati dell'agente; apprendimento di reward modellate tramite minimizzazione della divergenza; SVPG con JS-kernel per l'esplorazione basata sulla popolazione."]} +{"source": "We study the precise mechanisms which allow autoencoders to encode and decode a simple geometric shape, the disk.In this carefully controlled setting, we are able to describe the specific form of the optimal solution to the minimisation problem of the training step.We show that the autoencoder indeed approximates this solution during training.Secondly, we identify a clear failure in the generalisation capacity of the autoencoder, namely its inability to interpolate data.Finally, we explore several regularisation schemes to resolve the generalisation problem.Given the great attention that has been recently given to the generative capacity of neural networks, we believe that studying in depth simple geometric cases sheds some light on the generation process and can provide a minimal requirement experimental setup for more complex architectures.", "target": ["Studiamo il funzionamento degli autoencoder in un ambiente semplice e consigliamo nuove strategie per la loro regolarizzazione al fine di ottenere una migliore generalizzazione con interpolazione latente per la sintesi di immagini."]} +{"source": "We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know.These techniques open a wide range of potential applications such as cross-language communication, language learning or automatic video dubbing.We call this general problem multi-language speaker-conditioned speech synthesis and we present a simple but strong baseline for it.Our model architecture is similar to the encoder-decoder Char2Wav model or Tacotron.The main difference is that, instead of conditioning on characters or phonemes that are specific to a given language, we condition on a shared phonetic representation that is universal to all languages.This cross-language phonetic representation of text allows to synthesize speech in any language while preserving the vocal characteristics of the original speaker.Furthermore, we show that fine-tuning the weights of our model allows us to extend our results to speakers outside of the training dataset.", "target": ["Presentiamo un'idea semplice che permette di registrare uno speaker in una data lingua e sintetizzare la sua voce in altre lingue che potrebbe anche non conoscere."]} +{"source": "The goal of imitation learning (IL) is to enable a learner to imitate expert behavior given expert demonstrations.Recently, generative adversarial imitation learning (GAIL) has shown significant progress on IL for complex continuous tasks.However, GAIL and its extensions require a large number of environment interactions during training.In real-world environments, the more an IL method requires the learner to interact with the environment for better imitation, the more training time it requires, and the more damage it causes to the environments and the learner itself.We believe that IL algorithms could be more applicable to real-world problems if the number of interactions could be reduced. In this paper, we propose a model-free IL algorithm for continuous control.Our algorithm is made up mainly three changes to the existing adversarial imitation learning (AIL) methods –(a) adopting off-policy actor-critic (Off-PAC) algorithm to optimize the learner policy,(b) estimating the state-action value using off-policy samples without learning reward functions, and(c) representing the stochastic policy function so that its outputs are bounded.Experimental results show that our algorithm achieves competitive results with GAIL while significantly reducing the environment interactions.", "target": ["In questo articolo, abbiamo proposto un algoritmo IL senza modello e senza policy per il controllo continuo. I risultati sperimentali hanno mostrato che il nostro algoritmo raggiunge risultati competitivi con GAIL, riducendo significativamente le interazioni con l'ambiente."]} +{"source": "The dominant approach to unsupervised \"style transfer'' in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its \"style''.In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations.We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation.Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space.Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes.", "target": ["Un sistema di riscrittura del testo condizionato da più attributi controllabili"]} +{"source": "Vanilla RNN with ReLU activation have a simple structure that is amenable to systematic dynamical systems analysis and interpretation, but they suffer from the exploding vs. vanishing gradients problem.Recent attempts to retain this simplicity while alleviating the gradient problem are based on proper initialization schemes or orthogonality/unitary constraints on the RNN’s recurrency matrix, which, however, comes with limitations to its expressive power with regards to dynamical systems phenomena like chaos or multi-stability.Here, we instead suggest a regularization scheme that pushes part of the RNN’s latent subspace toward a line attractor configuration that enables long short-term memory and arbitrarily slow time scales.We show that our approach excels on a number of benchmarks like the sequential MNIST or multiplication problems, and enables reconstruction of dynamical systems which harbor widely different time scales.", "target": ["Sviluppiamo un nuovo approccio di ottimizzazione per la RNN basata sulla ReLU standard che permette la memoria a breve termine e l'identificazione di sistemi dinamici non lineari arbitrari con scale temporali molto diverse."]} +{"source": "In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.Wasserstein GANs have largely promoted the stability over the original GANs by introducing Wasserstein distance, but still remain unstable and are prone to a variety of failure modes.In this paper, we present a general framework named Wasserstein-Bounded GAN (WBGAN), which improves a large family of WGAN-based approaches by simply adding an upper-bound constraint to the Wasserstein term.Furthermore, we show that WBGAN can reasonably measure the difference of distributions which almost have no intersection.Experiments demonstrate that WBGAN can stabilize as well as accelerate convergence in the training processes of a series of WGAN-based variants.", "target": ["Proporre un framework migliorato per le WGAN e dimostrare le sue migliori prestazioni nella teoria e nella pratica."]} +{"source": "Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such as uniform or Gaussian.After a trained model is obtained, one can sample the Generator in various forms for exploration and understanding, such as interpolating between two samples, sampling in the vicinity of a sample or exploring differences between a pair of samples applied to a third sample.In this paper, we show that the latent space operations used in the literature so far induce a distribution mismatch between the resulting outputs and the prior distribution the model was trained on.To address this, we propose to use distribution matching transport maps to ensure that such latent space operations preserve the prior distribution, while minimally modifying the original operation. Our experimental results validate that the proposed operations give higher quality samples compared to the original operations.", "target": ["Le operazioni nello spazio latente GAN possono indurre un mismatch di distribuzione rispetto alla distribuzione di training, e noi affrontiamo questo problema usando un trasporto ottimale per far corrispondere le distribuzioni."]} +{"source": "Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, code completion, and fault localization.However, most existing program embeddings are based on syntactic features of programs, such as token sequences or abstract syntax trees.Unlike images and text, a program has well-defined semantics that can be difficult to capture by only considering its syntax (i.e. syntactically similar programs can exhibit vastly different run-time behavior), which makes syntax-based program embeddings fundamentally limited.We propose a novel semantic program embedding that is learned from program execution traces.Our key insight is that program states expressed as sequential tuples of live variable values not only capture program semantics more precisely, but also offer a more natural fit for Recurrent Neural Networks to model.We evaluate different syntactic and semantic program embeddings on the task of classifying the types of errors that students make in their submissions to an introductory programming class and on the CodeHunt education platform.Our evaluation results show that the semantic program embeddings significantly outperform the syntactic program embeddings based on token sequences and abstract syntax trees.In addition, we augment a search-based program repair system with predictions made from our semantic embedding and demonstrate significantly improved search efficiency.", "target": ["Un nuovo modo di imparare l'embedding semantico dei programmi"]} +{"source": "In this paper, we propose a generalization of the BN algorithm, diminishing batch normalization (DBN), where we update the BN parameters in a diminishing moving average way.Batch normalization (BN) is very effective in accelerating the convergence of a neural network training phase that it has become a common practice. Our proposed DBN algorithm remains the overall structure of the original BN algorithm while introduces a weighted averaging update to some trainable parameters. We provide an analysis of the convergence of the DBN algorithm that converges to a stationary point with respect to trainable parameters.Our analysis can be easily generalized for original BN algorithm by setting some parameters to constant.To the best knowledge of authors, this analysis is the first of its kind for convergence with Batch Normalization introduced.We analyze a two-layer model with arbitrary activation function. The primary challenge of the analysis is the fact that some parameters are updated by gradient while others are not. The convergence analysis applies to any activation function that satisfies our common assumptions.For the analysis, we also show the sufficient and necessary conditions for the stepsizes and diminishing weights to ensure the convergence. In the numerical experiments, we use more complex models with more layers and ReLU activation.We observe that DBN outperforms the original BN algorithm on Imagenet, MNIST, NI and CIFAR-10 datasets with reasonable complex FNN and CNN models.", "target": ["Proponiamo un'estensione della batch normalization, mostriamo una prima analisi di convergenza per questa estensione e dimostriamo in esperimenti numerici che ha prestazioni migliori della batch normalization originale."]} +{"source": "Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such as uniform or Gaussian.After a trained model is obtained, one can sample the Generator in various forms for exploration and understanding, such as interpolating between two samples, sampling in the vicinity of a sample or exploring differences between a pair of samples applied to a third sample.However, the latent space operations commonly used in the literature so far induce a distribution mismatch between the resulting outputs and the prior distribution the model was trained on.Previous works have attempted to reduce this mismatch with heuristic modification to the operations or by changing the latent distribution and re-training models.In this paper, we propose a framework for modifying the latent space operations such that the distribution mismatch is fully eliminated.Our approach is based on optimal transport maps, which adapt the latent space operations such that they fully match the prior distribution, while minimally modifying the original operation.Our matched operations are readily obtained for the commonly used operations and distributions and require no adjustment to the training procedure.", "target": ["Proponiamo un framework per modificare le operazioni dello spazio latente in modo tale che il mismatch della distribuzione tra gli output risultanti e la distribuzione a priori su cui è stato addestrato il modello generativo sia completamente eliminato."]} +{"source": "The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research.Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system.To overcome this problem, we propose an end to end multi-stream deep learning architecture which learns unified embeddings for query-response pairs by leveraging contextual information from memory networks and syntactic information by incorporating Graph Convolution Networks (GCN) over their dependency parse.A stream of this network also utilizes transfer learning by pre-training a bidirectional transformer to extract semantic representation for each input sentence and incorporates external knowledge through the neighbourhood of the entities from a Knowledge Base (KB).We benchmark these embeddings on next sentence prediction task and significantly improve upon the existing techniques.Furthermore, we use AMUSED to represent query and responses along with its context to develop a retrieval based conversational agent which has been validated by expert linguists to have comprehensive engagement with humans.", "target": ["Questo articolo fornisce un approccio multi-stream end-to-end per apprendere embedding unificati per coppie di domande-risposte nei sistemi di dialogo, sfruttando insieme informazioni contestuali, sintattiche, semantiche ed esterne."]} +{"source": "We examine techniques for combining generalized policies with search algorithms to exploit the strengths and overcome the weaknesses of each when solving probabilistic planning problems.The Action Schema Network (ASNet) is a recent contribution to planning that uses deep learning and neural networks to learn generalized policies for probabilistic planning problems.ASNets are well suited to problems where local knowledge of the environment can be exploited to improve performance, but may fail to generalize to problems they were not trained on.Monte-Carlo Tree Search (MCTS) is a forward-chaining state space search algorithm for optimal decision making which performs simulations to incrementally build a search tree and estimate the values of each state.Although MCTS can achieve state-of-the-art results when paired with domain-specific knowledge, without this knowledge, MCTS requires a large number of simulations in order to obtain reliable estimates in the search tree.By combining ASNets with MCTS, we are able to improve the capability of an ASNet to generalize beyond the distribution of problems it was trained on, as well as enhance the navigation of the search space by MCTS.", "target": ["Tecniche per combinare policy generalizzate con algoritmi di ricerca per sfruttare i punti di forza e superare le debolezze di ciascuno quando si risolvono problemi di pianificazione probabilistica"]} +{"source": "Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice.When the train and test distributions are mismatched, accuracy can plummet.Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment.In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers.We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions.AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.", "target": ["Otteniamo lo stato dell'arte sulla robustezza ai data-shift, e manteniamo la calibrazione sotto data-shift anche quando la precisione scende"]} +{"source": "Automatic Piano Fingering is a hard task which computers can learn using data.As data collection is hard and expensive, we propose to automate this process by automatically extracting fingerings from public videos and MIDI files, using computer-vision techniques.Running this process on 90 videos results in the largest dataset for piano fingering with more than 150K notes.We show that when running a previously proposed model for automatic piano fingering on our dataset and then fine-tuning it on manually labeled piano fingering data, we achieve state-of-the-art results.In addition to the fingering extraction method, we also introduce a novel method for transferring deep-learning computer-vision models to work on out-of-domain data, by fine-tuning it on out-of-domain augmentation proposed by a Generative Adversarial Network (GAN).For demonstration, we anonymously release a visualization of the output of our process for a single video on https://youtu.be/Gfs1UWQhr5Q", "target": ["Estraiamo automaticamente le informazioni sulla diteggiatura dai video delle esecuzioni di pianoforte, per essere utilizzati in modelli di predizione automatica della diteggiatura."]} +{"source": "Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.A recent approach for finding a common representation of the two domains is via domain adversarial training (Ganin & Lempitsky, 2015), which attempts to induce a feature extractor that matches the source and target feature distributions in some feature space.However, domain adversarial training faces two critical limitations:1) if the feature extraction function has high-capacity, then feature distribution matching is a weak constraint,2) in non-conservative domain adaptation (where no single classifier can perform well in both the source and target domains), training the model to do well on the source domain hurts performance on the target domain.In this paper, we address these issues through the lens of the cluster assumption, i.e., decision boundaries should not cross high-density data regions.We propose two novel and related models:1) the Virtual Adversarial Domain Adaptation (VADA) model, which combines domain adversarial training with a penalty term that punishes the violation the cluster assumption;2) the Decision-boundary Iterative Refinement Training with a Teacher (DIRT-T) model, which takes the VADA model as initialization and employs natural gradient steps to further minimize the cluster assumption violation.Extensive empirical results demonstrate that the combination of these two models significantly improve the state-of-the-art performance on the digit, traffic sign, and Wi-Fi recognition domain adaptation benchmarks.", "target": ["SOTA su domain adaptation unsupervised sfruttando l'ipotesi del cluster."]} +{"source": "In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model complex distributions of graph-structured data. Once learned, the model can be applied to an arbitrary graph, defining a probability density over the random variables represented by the graph.It is formulated as an ordinary differential equation system with shared and reusable functions that operate over the graphs. This leads to a new type of neural graph message passing scheme that performs continuous message passing over time.This class of models offers several advantages: a flexible representation that can generalize to variable data dimensions; ability to model dependencies in complex data distributions; reversible and memory-efficient; and exact and efficient computation of the likelihood of the data.We demonstrate the effectiveness of our model on a diverse set of generation tasks across different domains: graph generation, image puzzle generation, and layout generation from scene graphs.Our proposed model achieves significantly better performance compared to state-of-the-art models.", "target": ["Modelli generativi con grafi basati sulla generalizzazione del passaggio dei messaggi al tempo continuo usando equazioni differenziali ordinarie"]} +{"source": "The practical successes of deep neural networks have not been matched by theoretical progress that satisfyingly explains their behavior.In this work, we study the information bottleneck (IB) theory of deep learning, which makes three specific claims: first, that deep networks undergo two distinct phases consisting of an initial fitting phase and a subsequent compression phase; second, that the compression phase is causally related to the excellent generalization performance of deep networks; and third, that the compression phase occurs due to the diffusion-like behavior of stochastic gradient descent.Here we show that none of these claims hold true in the general case.Through a combination of analytical results and simulation, we demonstrate that the information plane trajectory is predominantly a function of the neural nonlinearity employed: double-sided saturating nonlinearities like tanh yield a compression phase as neural activations enter the saturation regime, but linear activation functions and single-sided saturating nonlinearities like the widely used ReLU in fact do not.Moreover, we find that there is no evident causal connection between compression and generalization: networks that do not compress are still capable of generalization, and vice versa.Next, we show that the compression phase, when it exists, does not arise from stochasticity in training by demonstrating that we can replicate the IB findings using full batch gradient descent rather than stochastic gradient descent.Finally, we show that when an input domain consists of a subset of task-relevant and task-irrelevant information, hidden representations do compress the task-irrelevant information, although the overall information about the input may monotonically increase with training time, and that this compression happens concurrently with the fitting process rather than during a subsequent compression period.", "target": ["Mostriamo che diverse affermazioni della teoria del collo di bottiglia informativo del deep learning non sono vere nel caso generale."]} +{"source": "Over the past four years, neural networks have been proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions.We show that adversarial vulnerability increases with the gradients of the training objective when viewed as a function of the inputs.For most current network architectures, we prove that the L1-norm of these gradients grows as the square root of the input size.These nets therefore become increasingly vulnerable with growing image size.Our proofs rely on the network’s weight distribution at initialization, but extensive experiments confirm that our conclusions still hold after usual training.", "target": ["Le reti neurali hanno grandi gradienti per costruzione; questo le rende vulnerabili agli avversari."]} +{"source": "Effective performance of neural networks depends critically on effective tuning of optimization hyperparameters, especially learning rates (and schedules thereof).We present Amortized Proximal Optimization (APO), which takes the perspective that each optimization step should approximately minimize a proximal objective (similar to the ones used to motivate natural gradient and trust region policy optimization).Optimization hyperparameters are adapted to best minimize the proximal objective after one weight update.We show that an idealized version of APO (where an oracle minimizes the proximal objective exactly) achieves global convergence to stationary point and locally second-order convergence to global optimum for neural networks.APO incurs minimal computational overhead.We experiment with using APO to adapt a variety of optimization hyperparameters online during training, including (possibly layer-specific) learning rates, damping coefficients, and gradient variance exponents.For a variety of network architectures and optimization algorithms (including SGD, RMSprop, and K-FAC), we show that with minimal tuning, APO performs competitively with carefully tuned optimizers.", "target": ["Introduciamo l'amortized proximal optimization (APO), un metodo per adattare una varietà di iperparametri di ottimizzazione online durante il training, compresi i learning rate, i coefficienti di smorzamento e gli esponenti di varianza del gradiente."]} +{"source": "Dense word vectors have proven their values in many downstream NLP tasks over the past few years.However, the dimensions of such embeddings are not easily interpretable.Out of the d-dimensions in a word vector, we would not be able to understand what high or low values mean.Previous approaches addressing this issue have mainly focused on either training sparse/non-negative constrained word embeddings, or post-processing standard pre-trained word embeddings.On the other hand, we analyze conventional word embeddings trained with Singular Value Decomposition, and reveal similar interpretability.We use a novel eigenvector analysis method inspired from Random Matrix Theory and show that semantically coherent groups not only form in the row space, but also the column space.This allows us to view individual word vector dimensions as human-interpretable semantic features.", "target": ["Senza richiedere alcun vincolo o post-elaborazione, dimostriamo che le dimensioni salienti dei word vector possono essere interpretate come feature semantiche."]} +{"source": "Neural networks in the brain and in neuromorphic chips confer systems with the ability to perform multiple cognitive tasks.However, both kinds of networks experience a wide range of physical perturbations, ranging from damage to edges of the network to complete node deletions, that ultimately could lead to network failure.A critical question is to understand how the computational properties of neural networks change in response to node-damage and whether there exist strategies to repair these networks in order to compensate for performance degradation.Here, we study the damage-response characteristics of two classes of neural networks, namely multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) trained to classify images from MNIST and CIFAR-10 datasets respectively.We also propose a new framework to discover efficient repair strategies to rescue damaged neural networks.The framework involves defining damage and repair operators for dynamically traversing the neural networks loss landscape, with the goal of mapping its salient geometric features.Using this strategy, we discover features that resemble path-connected attractor sets in the loss landscape.We also identify that a dynamic recovery scheme, where networks are constantly damaged and repaired, produces a group of networks resilient to damage as it can be quickly rescued.Broadly, our work shows that we can design fault-tolerant networks by applying on-line retraining consistently during damage for real-time applications in biology and machine learning.", "target": ["strategia per riparare le reti neurali danneggiate"]} +{"source": "Automatic question generation from paragraphs is an important and challenging problem, particularly due to the long context from paragraphs.In this paper, we propose and study two hierarchical models for the task of question generation from paragraphs.Specifically, we propose(a) a novel hierarchical BiLSTM model with selective attention and(b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs. We model a paragraph in terms of its constituent sentences, and a sentence in terms of its constituent words.While the introduction of the attention mechanism benefits the hierarchical BiLSTM model, the hierarchical Transformer, with its inherent attention and positional encoding mechanisms also performs better than flat transformer model.We conducted empirical evaluation on the widely used SQuAD and MS MARCO datasets using standard metrics. The results demonstrate the overall effectiveness of the hierarchical models over their flat counterparts. Qualitatively, our hierarchical models are able to generate fluent and relevant questions.", "target": ["Generazione automatica di domande da paragrafi utilizzando modelli gerarchici"]} +{"source": "Many deployed learned models are black boxes: given input, returns output.Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable.This work shows that such attributes of neural networks can be exposed from a sequence of queries.This has multiple implications.On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks -- we show that the revealed internal information helps generate more effective adversarial examples against the black box model.On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples.Our paper suggests that it is actually hard to draw a line between white box and black box models.", "target": ["L'interrogazione di una rete neurale black-box rivela molte informazioni su di essa; proponiamo nuovi \"metamodelli\" per estrarre efficacemente informazioni da una black box."]} +{"source": "Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP).A novel approach using variational inference for learning the reward function is proposed in this research.Using this technique, the intractable posterior distribution of the continuous latent variable (the reward function in this case) is analytically approximated to appear to be as close to the prior belief while trying to reconstruct the future state conditioned on the current state and action.The reward function is derived using a well-known deep generative model known as Conditional Variational Auto-encoder (CVAE) with Wasserstein loss function, thus referred to as Conditional Wasserstein Auto-encoder-IRL (CWAE-IRL), which can be analyzed as a combination of the backward and forward inference.This can then form an efficient alternative to the previous approaches to IRL while having no knowledge of the system dynamics of the agent.Experimental results on standard benchmarks such as objectworld and pendulum show that the proposed algorithm can effectively learn the latent reward function in complex, high-dimensional environments.", "target": ["Utilizzo di un framework supervisionato di modellazione della variabile latente per determinare la reward in un task di inverse reinforcement learning"]} +{"source": "The travelling salesman problem (TSP) is a well-known combinatorial optimization problem with a variety of real-life applications.We tackle TSP by incorporating machine learning methodology and leveraging the variable neighborhood search strategy.More precisely, the search process is considered as a Markov decision process (MDP), where a 2-opt local search is used to search within a small neighborhood, while a Monte Carlo tree search (MCTS) method (which iterates through simulation, selection and back-propagation steps), is used to sample a number of targeted actions within an enlarged neighborhood.This new paradigm clearly distinguishes itself from the existing machine learning (ML) based paradigms for solving the TSP, which either uses an end-to-end ML model, or simply applies traditional techniques after ML for post optimization.Experiments based on two public data sets show that, our approach clearly dominates all the existing learning based TSP algorithms in terms of performance, demonstrating its high potential on the TSP.More importantly, as a general framework without complicated hand-crafted rules, it can be readily extended to many other combinatorial optimization problems.", "target": ["Questo articolo combina la Monte Carlo tree search con la ricerca locale a 2-opt in una modalità a neighborhood variabile per risolvere efficacemente il TSP."]} +{"source": "Significant strides have been made toward designing better generative models in recent years.Despite this progress, however, state-of-the-art approaches are still largely unable to capture complex global structure in data.For example, images of buildings typically contain spatial patterns such as windows repeating at regular intervals; state-of-the-art generative methods can’t easily reproduce these structures.We propose to address this problem by incorporating programs representing global structure into the generative model—e.g., a 2D for-loop may represent a configuration of windows.Furthermore, we propose a framework for learning these models by leveraging program synthesis to generate training data.On both synthetic and real-world data, we demonstrate that our approach is substantially better than the state-of-the-art at both generating and completing images that contain global structure.", "target": ["Applicare la sintesi del programma ai task di completamento e generazione di immagini all'interno di un framework di deep learning"]} +{"source": "Learning control policies in robotic tasks requires a large number of interactions due to small learning rates, bounds on the updates or unknown constraints.In contrast humans can infer protective and safe solutions after a single failure or unexpected observation. In order to reach similar performance, we developed a hierarchical Bayesian optimization algorithm that replicates the cognitive inference and memorization process for avoiding failures in motor control tasks.A Gaussian Process implements the modeling and the sampling of the acquisition function.This enables rapid learning with large learning rates while a mental replay phase ensures that policy regions that led to failures are inhibited during the sampling process. The features of the hierarchical Bayesian optimization method are evaluated in a simulated and physiological humanoid postural balancing task.We quantitatively compare the human learning performance to our learning approach by evaluating the deviations of the center of mass during training.Our results show that we can reproduce the efficient learning of human subjects in postural control tasks which provides a testable model for future physiological motor control tasks.In these postural control tasks, our method outperforms standard Bayesian Optimization in the number of interactions to solve the task, in the computational demands and in the frequency of observed failures.", "target": ["Questo articolo presenta un modello computazionale per un efficiente adattamento del controllo posturale umano basato su funzioni di acquisizione gerarchiche con feature ben note."]} +{"source": "Deep reinforcement learning has achieved great success in many previously difficult reinforcement learning tasks, yet recent studies show that deep RL agents are also unavoidably susceptible to adversarial perturbations, similar to deep neural networks in classification tasks.Prior works mostly focus on model-free adversarial attacks and agents with discrete actions.In this work, we study the problem of continuous control agents in deep RL with adversarial attacks and propose the first two-step algorithm based on learned model dynamics.Extensive experiments on various MuJoCo domains (Cartpole, Fish, Walker, Humanoid) demonstrate that our proposed framework is much more effective and efficient than model-free based attacks baselines in degrading agent performance as well as driving agents to unsafe states.", "target": ["Studiamo il problema degli agenti di controllo continuo nella RL profonda con adversarial attack e abbiamo proposto un algoritmo in due fasi basato sulla dinamica del modello appreso."]} +{"source": "A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity.We formally define the notion of incremental learning dynamics and derive the conditions on depth and initialization for which this phenomenon arises in deep linear models.Our main theoretical contribution is a dynamical depth separation result, proving that while shallow models can exhibit incremental learning dynamics, they require the initialization to be exponentially small for these dynamics to present themselves.However, once the model becomes deeper, the dependence becomes polynomial and incremental learning can arise in more natural settings.We complement our theoretical findings by experimenting with deep matrix sensing, quadratic neural networks and with binary classification using diagonal and convolutional linear networks, showing all of these models exhibit incremental learning.", "target": ["Studiamo il bias che induce la sparsità dei modelli profondi, causato dalle loro dinamiche di apprendimento."]} +{"source": "Generative modeling of high dimensional data like images is a notoriously difficult and ill-defined problem.In particular, how to evaluate a learned generative model is unclear.In this paper, we argue that *adversarial learning*, pioneered with generative adversarial networks (GANs), provides an interesting framework to implicitly define more meaningful task losses for unsupervised tasks, such as for generating \"visually realistic\" images.By relating GANs and structured prediction under the framework of statistical decision theory, we put into light links between recent advances in structured prediction theory and the choice of the divergence in GANs.We argue that the insights about the notions of \"hard\" and \"easy\" to learn losses can be analogously extended to adversarial divergences.We also discuss the attractive properties of parametric adversarial divergences for generative modeling, and perform experiments to show the importance of choosing a divergence that reflects the final task.", "target": ["Le divergenze parametriche adversarial definiscono implicitamente loss di task più significative per la modellazione generativa, facciamo dei paralleli con la predizione strutturata per studiare le proprietà di queste divergenze e la loro capacità di codificare il task di interesse."]} +{"source": "Experimental reproducibility and replicability are critical topics in machine learning.Authors have often raised concerns about their lack in scientific publications to improve the quality of the field.Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works.As such, several Graph Neural Network models have been developed to effectively tackle graph classification.However, experimental procedures often lack rigorousness and are hardly reproducible.Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art.To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks.Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet.We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.", "target": ["Forniamo un confronto rigoroso di diverse graph neural network per la classificazione dei grafi."]} +{"source": "Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, especially with a limited training dataset.In images, DA is usually based on heuristic transformations, like geometric or color transformations.Instead of using predefined transformations, our work learns data augmentation directly from the training data by learning to transform images with an encoder-decoder architecture combined with a spatial transformer network.The transformed images still belong to the same class, but are new, more complex samples for the classifier.Our experiments show that our approach is better than previous generative data augmentation methods, and comparable to predefined transformation methods when training an image classifier.", "target": ["Apprendimento automatico della data augmentation utilizzando un'architettura basata su GAN per migliorare un classificatore di immagini"]} +{"source": "We consider the problem of information compression from high dimensional data.Where many studies consider the problem of compression by non-invertible trans- formations, we emphasize the importance of invertible compression.We introduce new class of likelihood-based auto encoders with pseudo bijective architecture, which we call Pseudo Invertible Encoders.We provide the theoretical explanation of their principles.We evaluate Gaussian Pseudo Invertible Encoder on MNIST, where our model outperform WAE and VAE in sharpness of the generated images.", "target": ["Nuova classe di autoencoder con architettura pseudo invertibile"]} +{"source": "We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems. The approach reaches current state-of-the-art methods on MNIST and provides reasonable performances on SVHN and CIFAR10.Through the introduced method, residual networks are for the first time applied to semi-supervised tasks.Experiments with one-dimensional signals highlight the generality of the method.Importantly, our approach is simple, efficient, and requires no change in the deep network architecture.", "target": ["Sfruttiamo uno schema di inversione per deep neural network arbitrarie per sviluppare un nuovo framework di apprendimento semi-supervised applicabile a molte topologie."]} +{"source": "Deep learning has become a widely used tool in many computational and classification problems. Nevertheless obtaining and labeling data, which is needed for strong results, is often expensive or even not possible. In this paper three different algorithmic approaches to deal with limited access to data are evaluated and compared to each other. We show the drawbacks and benefits of each method. One successful approach, especially in one- or few-shot learning tasks, is the use of external data during the classification task. Another successful approach, which achieves state of the art results in semi-supervised learning (SSL) benchmarks, is consistency regularization.Especially virtual adversarial training (VAT) has shown strong results and will be investigated in this paper. The aim of consistency regularization is to force the network not to change the output, when the input or the network itself is perturbed.Generative adversarial networks (GANs) have also shown strong empirical results. In many approaches the GAN architecture is used in order to create additional data and therefor to increase the generalization capability of the classification network.Furthermore we consider the use of unlabeled data for further performance improvement. The use of unlabeled data is investigated both for GANs and VAT.", "target": ["Confronto tra reti neurali siamesi, GAN e IVA per few shot learning."]} +{"source": "This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives.First, it puts forward a novel concept of \"History of Word\" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation.Second, it identifies an attention scoring function that better utilizes the \"history of word\" concept.Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer.We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017).Meanwhile, we verify the generalization of FusionNet with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%.", "target": ["Proponiamo un miglioramento light-weight per l'attention e un'architettura neurale, FusionNet, per ottenere SotA su SQuAD e SQuAD adversarial."]} +{"source": "We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model.Both the generative and inference model are trained using the adversarial learning paradigm.We demonstrate that the hierarchical structure supports the learning of progressively more abstract representations as well as providing semantically meaningful reconstructions with different levels of fidelity.Furthermore, we show that minimizing the Jensen-Shanon divergence between the generative and inference network is enough to minimize the reconstruction error. The resulting semantically meaningful hierarchical latent structure discovery is exemplified on the CelebA dataset. There, we show that the features learned by our model in an unsupervised way outperform the best handcrafted features. Furthermore, the extracted features remain competitive when compared to several recent deep supervised approaches on an attribute prediction task on CelebA.Finally, we leverage the model's inference network to achieve state-of-the-art performance on a semi-supervised variant of the MNIST digit classification task.", "target": ["Modello generativo gerarchico addestrato in modo adversarial con rappresentazione latente robusta e appresa semanticamente."]} +{"source": "Conservation laws are considered to be fundamental laws of nature.It has broad application in many fields including physics, chemistry, biology, geology, and engineering.Solving the differential equations associated with conservation laws is a major branch in computational mathematics.Recent success of machine learning, especially deep learning, in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods.In this paper, we are the first to explore the possibility and benefit of solving nonlinear conservation laws using deep reinforcement learning.As a proof of concept, we focus on 1-dimensional scalar conservation laws.We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner.We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision making process and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach.In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts.Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and how well it generalizes.Our code is released anomynously at \\url{https://github.com/qwerlanksdf/L2D}.", "target": ["Osserviamo che i risolutori numerici di PDE possono essere considerati come processi decisionali di Markov, e proponiamo di usare il Reinforcement Learning per risolvere leggi di conservazione scalari 1D"]} +{"source": "We present a neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations.Instead of the deconvolutional network typically used in the decoder of VAEs, we tile (broadcast) the latent vector across space, concatenate fixed X- and Y-“coordinate” channels, and apply a fully convolutional network with 1x1 stride.This provides an architectural prior for dissociating positional from non-positional features in the latent space, yet without providing any explicit supervision to this effect.We show that this architecture, which we term the Spatial Broadcast decoder, improves disentangling, reconstruction accuracy, and generalization to held-out regions in data space. We show the Spatial Broadcast Decoder is complementary to state-of-the-art (SOTA) disentangling techniques and when incorporated improves their performance.", "target": ["Introduciamo un'architettura di rendering neurale che aiuta i VAE ad apprendere rappresentazioni latenti disentangled."]} +{"source": "Similar to humans and animals, deep artificial neural networks exhibit critical periods during which a temporary stimulus deficit can impair the development of a skill.The extent of the impairment depends on the onset and length of the deficit window, as in animal models, and on the size of the neural network.Deficits that do not affect low-level statistics, such as vertical flipping of the images, have no lasting effect on performance and can be overcome with further training. To better understand this phenomenon, we use the Fisher Information of the weights to measure the effective connectivity between layers of a network during training. Counterintuitively, information rises rapidly in the early phases of training, and then decreases, preventing redistribution of information resources in a phenomenon we refer to as a loss of \"Information Plasticity\". Our analysis suggests that the first few epochs are critical for the creation of strong connections that are optimal relative to the input data distribution.Once such strong connections are created, they do not appear to change during additional training.These findings suggest that the initial learning transient, under-scrutinized compared to asymptotic behavior, plays a key role in determining the outcome of the training process.Our findings, combined with recent theoretical results in the literature, also suggest that forgetting (decrease of information in the weights) is critical to achieving invariance and disentanglement in representation learning.Finally, critical periods are not restricted to biological systems, but can emerge naturally in learning systems, whether biological or artificial, due to fundamental constrains arising from learning dynamics and information processing.", "target": ["I deficit sensoriali nelle prime fasi del training possono portare a una perdita irreversibile delle prestazioni sia nelle reti artificiali che in quelle neuronali, suggerendo fenomeni informativi come causa comune, e indicano l'importanza del transitorio iniziale e dell'oblio."]} +{"source": "We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during compressed architecture search.Given a teacher network, we search for a compressed network architecture by using Bayesian Optimization (BO) with a kernel function defined over our proposed embedding space to select architectures for evaluation.We demonstrate that our search algorithm can significantly outperform various baseline methods, such as random search and reinforcement learning (Ashok et al., 2018).The compressed architectures found by our method are also better than the state-of-the-art manually-designed compact architecture ShuffleNet (Zhang et al., 2018).We also demonstrate that the learned embedding space can be transferred to new settings for architecture search, such as a larger teacher network or a teacher network in a different architecture family, without any training.", "target": ["Proponiamo un metodo per imparare in modo incrementale uno spazio di embedding sul dominio delle architetture di rete, per permettere la selezione accurata delle architetture da valutare durante la ricerca di architetture compresse."]} +{"source": "Reinforcement Learning (RL) problem can be solved in two different ways - the Value function-based approach and the policy optimization-based approach - to eventually arrive at an optimal policy for the given environment.One of the recent breakthroughs in reinforcement learning is the use of deep neural networks as function approximators to approximate the value function or q-function in a reinforcement learning scheme.This has led to results with agents automatically learning how to play games like alpha-go showing better-than-human performance.Deep Q-learning networks (DQN) and Deep Deterministic Policy Gradient (DDPG) are two such methods that have shown state-of-the-art results in recent times.Among the many variants of RL, an important class of problems is where the state and action spaces are continuous --- autonomous robots, autonomous vehicles, optimal control are all examples of such problems that can lend themselves naturally to reinforcement based algorithms, and have continuous state and action spaces.In this paper, we adapt and combine approaches such as DQN and DDPG in novel ways to outperform the earlier results for continuous state and action space problems. We believe these results are a valuable addition to the fast-growing body of results on Reinforcement Learning, more so for continuous state and action space problems.", "target": ["Migliorare le prestazioni di un agente RL nel dominio continuo delle azioni e dello spazio di stato utilizzando la riproduzione prioritaria dell'esperienza e il rumore dei parametri."]} +{"source": "Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis.We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space.We show that an interaction tensor (attention weight) contains semantic information to solve natural language inference, and a denser interaction tensor contains richer semantic information.One instance of such architecture, Densely Interactive Inference Network (DIIN), demonstrates the state-of-the-art performance on large scale NLI copora and large-scale NLI alike corpus.It's noteworthy that DIIN achieve a greater than 20% error reduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to the strongest published system.", "target": ["Si mostra che il peso di attention multicanale contiene la feature semantica per risolvere il task di natural language inference."]} +{"source": "Determinantal Point Processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items.For this reason, they have gained prominence in many machine learning applications that rely on subset selection.However, sampling from a DPP over a ground set of size N is a costly operation, requiring in general an O(N^3) preprocessing cost and an O(Nk^3) sampling cost for subsets of size k. We approach this problem by introducing DppNets: generative deep models that produce DPP-like samples for arbitrary ground sets. We develop an inhibitive attention mechanism based on transformer networks that captures a notion of dissimilarity between feature vectors. We show theoretically that such an approximation is sensible as it maintains the guarantees of inhibition or dissimilarity that makes DPP so powerful and unique. Empirically, we demonstrate that samples from our model receive high likelihood under the more expensive DPP alternative.", "target": ["Approssimiamo i processi puntuali determinanti con le reti neurali; giustifichiamo il nostro modello teoricamente ed empiricamente."]} +{"source": "This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error.The whole pipeline is differentiable, so that the network can learn suitable features that make the BA problem more tractable.Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth.The network first generates several basis depth maps according to the input image, and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA.The basis depth maps generator is also learned via end-to-end training.The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and deep learning (i.e. feature learning and basis depth maps learning) to address the challenging dense SfM problem.Experiments on large scale real data prove the success of the proposed method.", "target": ["Questo articolo introduce un'architettura di rete per risolvere il problema della structure-from-motion attraverso l'aggiustamento del bundle di feature"]} +{"source": "Temporal Difference Learning with function approximation is known to be unstable.Previous work like \\citet{sutton2009fast} and \\citet{sutton2009convergent} has presented alternative objectives that are stable to minimize.However, in practice, TD-learning with neural networks requires various tricks like using a target network that updates slowly \\citep{mnih2015human}.In this work we propose a constraint on the TD update that minimizes change to the target values.This constraint can be applied to the gradients of any TD objective, and can be easily applied to nonlinear function approximation.We validate this update by applying our technique to deep Q-learning, and training without a target network.We also show that adding this constraint on Baird's counterexample keeps Q-learning from diverging.", "target": ["Mostriamo che l'aggiunta di un vincolo agli aggiornamenti TD stabilizza l'apprendimento e permette il deep Q learning senza una rete di destinazione"]} +{"source": "We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets.DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie - one from Wikipedia and the other from IMDb - written by two different authors.We asked crowdsourced workers to create questions from one version of the plot and a different set of workers to extract or synthesize corresponding answers from the other version.This unique characteristic of DuoRC where questions and answers are created from different versions of a document narrating the same underlying story, ensures by design, that there is very little lexical overlap between the questions created from one version and the segments containing the answer in the other version.Further, since the two versions have different level of plot detail, narration style, vocabulary, etc., answering questions from the second version requires deeper language understanding and incorporating background knowledge not available in the given text.Additionally, the narrative style of passages arising from movie plots (as opposed to typical descriptive passages in existing datasets) exhibits the need to perform complex reasoning over events across multiple sentences.Indeed, we observe that state-of-the-art neural RC models which have achieved near human performance on the SQuAD dataset, even when coupled with traditional NLP techniques to address the challenges presented in DuoRC exhibit very poor performance (F1 score of 37.42% on DuoRC v/s 86% on SQuAD dataset).This opens up several interesting research avenues wherein DuoRC could complement other Reading Comprehension style datasets to explore novel neural approaches for studying language understanding.", "target": ["Proponiamo DuoRC, un nuovo dataset per la comprensione della lettura (RC) che contiene 186.089 coppie di QA generate dall'uomo create da una collezione di 7680 coppie di trame cinematografiche parallele e introduciamo un task RC che consiste nel leggere una versione della trama e rispondere a domande create dall'altra versione; quindi, per construzione, richiede un ragionamento complesso e una comprensione linguistica più profonda per superare la scarsa sovrapposizione lessicale tra la trama e la domanda."]} +{"source": "We consider the problem of weakly supervised structured prediction (SP) with reinforcement learning (RL) – for example, given a database table and a question, perform a sequence of computation actions on the table, which generates a response and receives a binary success-failure reward. This line of research has been successful by leveraging RL to directly optimizes the desired metrics of the SP tasks – for example, the accuracy in question answering or BLEU score in machine translation. However, different from the common RL settings, the environment dynamics is deterministic in SP, which hasn’t been fully utilized by the model-freeRL methods that are usually applied.Since SP models usually have full access to the environment dynamics, we propose to apply model-based RL methods, which rely on planning as a primary model component.We demonstrate the effectiveness of planning-based SP with a Neural Program Planner (NPP), which, given a set of candidate programs from a pretrained search policy, decides which program is the most promising considering all the information generated from executing these programs.We evaluate NPP on weakly supervised program synthesis from natural language(semantic parsing) by stacked learning a planning module based on pretrained search policies.On the WIKITABLEQUESTIONS benchmark, NPP achieves a new state-of-the-art of 47.2% accuracy.", "target": ["Un componente di pianificazione basato sul modello migliora l'analisi semantica basata su RL su WikiTableQuestions."]} +{"source": "Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost.To address this cost, a number of quantization schemeshave been proposed - but most of these techniques focused on quantizing weights, which are relatively smaller in size compared to activations.This paper proposes a novel quantization scheme for activations during training - that enables neural networks to work well with ultra low precision weights and activations without any significant accuracy degradation. This technique, PArameterized Clipping acTi-vation (PACT), uses an activation clipping parameter α that is optimized duringtraining to find the right quantization scale.PACT allows quantizing activations toarbitrary bit precisions, while achieving much better accuracy relative to publishedstate-of-the-art quantization schemes.We show, for the first time, that both weights and activations can be quantized to 4-bits of precision while still achieving accuracy comparable to full precision networks across a range of popular models and datasets.We also show that exploiting these reduced-precision computational units in hardware can enable a super-linear improvement in inferencing performance dueto a significant reduction in the area of accelerator compute engines coupled with the ability to retain the quantized model and activation data in on-chip memories.", "target": ["Un nuovo modo di quantizzare l'attivazione delle deep neural network tramite il clipping parametrizzato che ottimizza la scala di quantizzazione tramite il gradient descent stocastico."]} +{"source": "Pruning neural network parameters is often viewed as a means to compress models, but pruning has also been motivated by the desire to prevent overfitting.This motivation is particularly relevant given the perhaps surprising observation that a wide variety of pruning approaches increase test accuracy despite sometimes massive reductions in parameter counts.To better understand this phenomenon, we analyze the behavior of pruning over the course of training, finding that pruning's effect on generalization relies more on the instability it generates (defined as the drops in test accuracy immediately following pruning) than on the final size of the pruned model.We demonstrate that even the pruning of unimportant parameters can lead to such instability, and show similarities between pruning and regularizing by injecting noise, suggesting a mechanism for pruning-based generalization improvements that is compatible with the strong generalization recently observed in over-parameterized networks.", "target": ["Dimostriamo che i metodi di pruning che introducono una maggiore instabilità nella loss conferiscono anche una migliore generalizzazione, ed esploriamo i meccanismi alla base di questo effetto."]} +{"source": "Neural networks trained with backpropagation, the standard algorithm of deep learning which uses weight transport, are easily fooled by existing gradient-based adversarial attacks.This class of attacks are based on certain small perturbations of the inputs to make networks misclassify them.We show that less biologically implausible deep neural networks trained with feedback alignment, which do not use weight transport, can be harder to fool, providing actual robustness.Tested on MNIST, deep neural networks trained without weight transport (1) have an adversarial accuracy of 98% compared to 0.03% for neural networks trained with backpropagation and (2) generate non-transferable adversarial examples.However, this gap decreases on CIFAR-10 but is still significant particularly for small perturbation magnitude less than 1 ⁄ 2.", "target": ["Reti neurali profonde meno biologicamente improbabili addestrate senza weight transfer possono essere più difficili da ingannare."]} +{"source": "Chemical reactions can be described as the stepwise redistribution of electrons in molecules.As such, reactions are often depicted using \"arrow-pushing\" diagrams which show this movement as a sequence of arrows.We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction data.Instead of predicting product molecules directly from reactant molecules in one shot, learning a model of electron movement has the benefits of(a) being easy for chemists to interpret,(b) incorporating constraints of chemistry, such as balanced atom counts before and after the reaction, and(c) naturally encoding the sparsity of chemical reactions, which usually involve changes in only a small number of atoms in the reactants.We design a method to extract approximate reaction paths from any dataset of atom-mapped reaction SMILES strings.Our model achieves excellent performance on an important subset of the USPTO reaction dataset, comparing favorably to the strongest baselines.Furthermore, we show that our model recovers a basic knowledge of chemistry without being explicitly trained to do so.", "target": ["Un modello generativo per la predizione delle reazioni che impara gli step meccanici elettronici di una reazione direttamente dai dati di reazione grezzi."]} +{"source": "When machine learning models are used for high-stakes decisions, they should predict accurately, fairly, and responsibly.To fulfill these three requirements, a model must be able to output a reject option (i.e. say \"``I Don't Know\") when it is not qualified to make a prediction.In this work, we propose learning to defer, a method by which a model can defer judgment to a downstream decision-maker such as a human user.We show that learning to defer generalizes the rejection learning framework in two ways: by considering the effect of other agents in the decision-making process, and by allowing for optimization of complex objectives.We propose a learning algorithm which accounts for potential biases held by decision-makerslater in a pipeline.Experiments on real-world datasets demonstrate that learningto defer can make a model not only more accurate but also less biased.Even whenoperated by highly biased users, we show thatdeferring models can still greatly improve the fairness of the entire pipeline.", "target": ["Incorporare la capacità di dire I-don't-know può migliorare la fairness di un classificatore senza sacrificare troppo la precisione, e questo miglioramento aumenta quando il classificatore ha una visione del processo decisionale a valle."]} +{"source": "Hierarchical Task Networks (HTN) generate plans using a decomposition process guided by extra domain knowledge to guide search towards a planning task.While many HTN planners can make calls to external processes (e.g. to a simulator interface) during the decomposition process, this is a computationally expensive process, so planner implementations often use such calls in an ad-hoc way using very specialized domain knowledge to limit the number of calls.Conversely, the few classical planners that are capable of using external calls (often called semantic attachments) during planning do so in much more limited ways by generating a fixed number of ground operators at problem grounding time.In this paper we develop the notion of semantic attachments for HTN planning using semi co-routines, allowing such procedurally defined predicates to link the planning process to custom unifications outside of the planner.The resulting planner can then use such co-routines as part of its backtracking mechanism to search through parallel dimensions of the state-space (e.g. through numeric variables).We show empirically that our planner outperforms the state-of-the-art numeric planners in a number of domains using minimal extra domain knowledge.", "target": ["Un approccio per eseguire la pianificazione HTN utilizzando procedure esterne per valutare i predicati in fase di esecuzione (semantic attachments)."]} +{"source": "Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks.Furthermore, when averaged word vectors are trained supervised on large corpora of paraphrases, they achieve state-of-the-art results on standard STS benchmarks.Inspired by these insights, we push the limits of word embeddings even further.We propose a novel fuzzy bag-of-words (FBoW) representation for text that contains all the words in the vocabulary simultaneously but with different degrees of membership, which are derived from similarities between word vectors.We show that max-pooled word vectors are only a special case of fuzzy BoW and should be compared via fuzzy Jaccard index rather than cosine similarity.Finally, we propose DynaMax, a completely unsupervised and non-parametric similarity measure that dynamically extracts and max-pools good features depending on the sentence pair.This method is both efficient and easy to implement, yet outperforms current baselines on STS tasks by a large margin and is even competitive with supervised word vectors trained to directly optimise cosine similarity.", "target": ["I vettori di parole max-pooled con similarità fuzzy Jaccard set sono una baseline estremamente competitiva per la similarità semantica; noi proponiamo una semplice variante dinamica che funziona ancora meglio."]} +{"source": "State-of-the-art results in imitation learning are currently held by adversarial methods that iteratively estimate the divergence between student and expert policies and then minimize this divergence to bring the imitation policy closer to expert behavior.Analogous techniques for imitation learning from observations alone (without expert action labels), however, have not enjoyed the same ubiquitous successes. Recent work in adversarial methods for generative models has shown that the measure used to judge the discrepancy between real and synthetic samples is an algorithmic design choice, and that different choices can result in significant differences in model performance.Choices including Wasserstein distance and various $f$-divergences have already been explored in the adversarial networks literature, while more recently the latter class has been investigated for imitation learning.Unfortunately, we find that in practice this existing imitation-learning framework for using $f$-divergences suffers from numerical instabilities stemming from the combination of function approximation and policy-gradient reinforcement learning.In this work, we alleviate these challenges and offer a reparameterization of adversarial imitation learning as $f$-divergence minimization before further extending the framework to handle the problem of imitation from observations only.Empirically, we demonstrate that our design choices for coupling imitation learning and $f$-divergences are critical to recovering successful imitation policies.Moreover, we find that with the appropriate choice of $f$-divergence, we can obtain imitation-from-observation algorithms that outperform baseline approaches and more closely match expert performance in continous-control tasks with low-dimensional observation spaces.With high-dimensional observations, we still observe a significant gap with and without action labels, offering an interesting avenue for future work.", "target": ["L'obiettivo generale di questo lavoro è di permettere un'imitazione sample-efficient da dimostrazioni di esperti, sia con che senza la fornitura di label di azioni di esperti, attraverso l'uso di f-divergenze."]} +{"source": "Momentary fluctuations in attention (perceptual accuracy) correlate with neural activity fluctuations in primate visual areas.Yet, the link between such momentary neural fluctuations and attention state remains to be shown in the human brain.We investigate this link using a real-time cognitive brain machine interface (cBMI) based on steady state visually evoked potentials (SSVEPs): occipital EEG potentials evoked by rhythmically flashing stimuli.Tracking momentary fluctuations in SSVEP power, in real-time, we presented stimuli time-locked to when this power reached (predetermined) high or low thresholds.We observed a significant increase in discrimination accuracy (d') when stimuli were triggered during high (versus low) SSVEP power epochs, at the location cued for attention.Our results indicate a direct link between attention’s effects on perceptual accuracy and and neural gain in EEG-SSVEP power, in the human brain.", "target": ["Con un'interfaccia cognitiva cervello-macchina, mostriamo un legame diretto tra gli effetti attenzionali sulla precisione percettiva e il guadagno neurale nella potenza EEG-SSVEP, nel cervello umano."]} +{"source": "Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.Inspired by the observation that the intrinsic dimension of image data is much smaller than its pixel space dimension and the vulnerability of neural networks grows with the input dimension, we propose to embed high-dimensional input images into a low-dimensional space to perform classification.However, arbitrarily projecting the input images to a low-dimensional space without regularization will not improve the robustness of deep neural networks.We propose a new framework, Embedding Regularized Classifier (ER-Classifier), which improves the adversarial robustness of the classifier through embedding regularization.Experimental results on several benchmark datasets show that, our proposed framework achieves state-of-the-art performance against strong adversarial attack methods.", "target": ["Un framework generale e facile da usare che migliora l'adversarial robustness dei modelli di classificazione profonda attraverso la regolarizzazione dell'embedding."]} +{"source": "We investigate task clustering for deep learning-based multi-task and few-shot learning in the settings with large numbers of diverse tasks.Our method measures task similarities using cross-task transfer performance matrix.Although this matrix provides us critical information regarding similarities between tasks, the uncertain task-pairs, i.e., the ones with extremely asymmetric transfer scores, may collectively mislead clustering algorithms to output an inaccurate task-partition.Moreover, when the number of tasks is large, generating the full transfer performance matrix can be very time consuming.To overcome these limitations, we propose a novel task clustering algorithm to estimate the similarity matrix based on the theory of matrix completion.The proposed algorithm can work on partially-observed similarity matrices based on only sampled task-pairs with reliable scores, ensuring its efficiency and robustness.Our theoretical analysis shows that under mild assumptions, the reconstructed matrix perfectly matches the underlying “true” similarity matrix with an overwhelming probability.The final task partition is computed by applying an efficient spectral clustering algorithm to the recovered matrix.Our results show that the new task clustering method can discover task clusters that benefit both multi-task learning and few-shot learning setups for sentiment classification and dialog intent classification tasks.", "target": ["Proponiamo un algoritmo di clustering basato sul completamento della matrice per il deep multi-task e few-shot learning nel setting con un gran numero di task diversi."]} +{"source": "The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention.In particular, tensor networks (TNs) and deep learning architectures bear striking similarities to the extent that TNs can be used for machine learning.Previous results used one-dimensional TNs in image recognition, showing limited scalability and a request of high bond dimension.In this work, we train two-dimensional hierarchical TNs to solve image recognition problems, using a training algorithm derived from the multipartite entanglement renormalization ansatz (MERA).This approach overcomes scalability issues and implies novel mathematical connections among quantum many-body physics, quantum information theory, and machine learning.While keeping the TN unitary in the training phase, TN states can be defined, which optimally encodes each class of the images into a quantum many-body state.We study the quantum features of the TN states, including quantum entanglement and fidelity.We suggest these quantities could be novel properties that characterize the image classes, as well as the machine learning tasks.Our work could be further applied to identifying possible quantum properties of certain artificial intelligence methods.", "target": ["Questo approccio supera i problemi di scalabilità e implica nuove connessioni matematiche tra la fisica quantistica a molti corpi, la teoria dell'informazione quantistica e l'apprendimento automatico."]} +{"source": "Predicting outcomes and planning interactions with the physical world are long-standing goals for machine learning.A variety of such tasks involves continuous physical systems, which can be described by partial differential equations (PDEs) with many degrees of freedom.Existing methods that aim to control the dynamics of such systems are typically limited to relatively short time frames or a small number of interaction parameters.We present a novel hierarchical predictor-corrector scheme which enables neural networks to learn to understand and control complex nonlinear physical systems over long time frames.We propose to split the problem into two distinct tasks: planning and control.To this end, we introduce a predictor network that plans optimal trajectories and a control network that infers the corresponding control parameters.Both stages are trained end-to-end using a differentiable PDE solver.We demonstrate that our method successfully develops an understanding of complex physical systems and learns to control them for tasks involving PDEs such as the incompressible Navier-Stokes equations.", "target": ["Addestriamo una combinazione di reti neurali per prevedere traiettorie ottimali per sistemi fisici complessi."]} +{"source": "The ability of overparameterized deep networks to generalize well has been linked to the fact that stochastic gradient descent (SGD) finds solutions that lie in flat, wide minima in the training loss -- minima where the output of the network is resilient to small random noise added to its parameters. So far this observation has been used to provide generalization guarantees only for neural networks whose parameters are either \\textit{stochastic} or \\textit{compressed}.In this work, we present a general PAC-Bayesian framework that leverages this observation to provide a bound on the original network learned -- a network that is deterministic and uncompressed. What enables us to do this is a key novelty in our approach: our framework allows us to show that if on training data, the interactions between the weight matrices satisfy certain conditions that imply a wide training loss minimum, these conditions themselves {\\em generalize} to the interactions between the matrices on test data, thereby implying a wide test loss minimum.We then apply our general framework in a setup where we assume that the pre-activation values of the network are not too small (although we assume this only on the training data).In this setup, we provide a generalization guarantee for the original (deterministic, uncompressed) network, that does not scale with product of the spectral norms of the weight matrices -- a guarantee that would not have been possible with prior approaches.", "target": ["Forniamo una garanzia di generalizzazione basata su PAC-Bayes per reti profonde non compresse e deterministiche generalizzando la resistenza al rumore della rete sui training data ai test data."]} +{"source": "Training neural networks to be certifiably robust is critical to ensure their safety against adversarial attacks.However, it is currently very difficult to train a neural network that is both accurate and certifiably robust.In this work we take a step towards addressing this challenge.We prove that for every continuous function $f$, there exists a network $n$ such that:(i) $n$ approximates $f$ arbitrarily close, and(ii) simple interval bound propagation of a region $B$ through $n$ yields a result that is arbitrarily close to the optimal output of $f$ on $B$.Our result can be seen as a Universal Approximation Theorem for interval-certified ReLU networks.To the best of our knowledge, this is the first work to prove the existence of accurate, interval-certified networks.", "target": ["Dimostriamo che per una grande classe di funzioni f esiste una rete robusta certificata da un intervallo che approssima f fino a una precisione arbitraria."]} +{"source": "In this paper, we propose an efficient framework to accelerate convolutional neural networks.We utilize two types of acceleration methods: pruning and hints.Pruning can reduce model size by removing channels of layers.Hints can improve the performance of student model by transferring knowledge from teacher model.We demonstrate that pruning and hints are complementary to each other.On one hand, hints can benefit pruning by maintaining similar feature representations.On the other hand, the model pruned from teacher networks is a good initialization for student model, which increases the transferability between two networks.Our approach performs pruning stage and hints stage iteratively to further improve theperformance.Furthermore, we propose an algorithm to reconstruct the parameters of hints layer and make the pruned model more suitable for hints.Experiments were conducted on various tasks including classification and pose estimation.Results on CIFAR-10, ImageNet and COCO demonstrate the generalization and superiority of our framework.", "target": ["Questo è un lavoro che mira a potenziare tutti i metodi di pruning e di imitazione esistenti."]} +{"source": "For typical sequence prediction problems such as language generation, maximum likelihood estimation (MLE) has commonly been adopted as it encourages the predicted sequence most consistent with the ground-truth sequence to have the highest probability of occurring.However, MLE focuses on once-to-all matching between the predicted sequence and gold-standard, consequently treating all incorrect predictions as being equally incorrect.We refer to this drawback as {\\it negative diversity ignorance} in this paper.Treating all incorrect predictions as equal unfairly downplays the nuance of these sequences' detailed token-wise structure.To counteract this, we augment the MLE loss by introducing an extra Kullback--Leibler divergence term derived by comparing a data-dependent Gaussian prior and the detailed training prediction.The proposed data-dependent Gaussian prior objective (D2GPo) is defined over a prior topological order of tokens and is poles apart from the data-independent Gaussian prior (L2 regularization) commonly adopted in smoothing the training of MLE.Experimental results show that the proposed method makes effective use of a more detailed prior in the data and has improved performance in typical language generation tasks, including supervised and unsupervised machine translation, text summarization, storytelling, and image captioning.", "target": ["Introduciamo un ulteriore obiettivo che è un prior gaussiano dipendente dai dati per aumentare l'attuale training MLE, che è progettato per catturare la prior knowledge nei dati ground-truth."]} +{"source": "We propose an interactive classification approach for natural language queries.Instead of classifying given the natural language query only, we ask the user for additional information using a sequence of binary and multiple-choice questions.At each turn, we use a policy controller to decide if to present a question or pro-vide the user the final answer, and select the best question to ask by maximizing the system information gain.Our formulation enables bootstrapping the system without any interaction data, instead relying on non-interactive crowdsourcing an-notation tasks.Our evaluation shows the interaction helps the system increase its accuracy and handle ambiguous queries, while our approach effectively balances the number of questions and the final accuracy.", "target": ["Proponiamo un approccio interattivo per classificare le query in linguaggio naturale chiedendo agli utenti informazioni aggiuntive utilizzando l'information gain e un controller di policy di reinforcement learning."]} +{"source": "Convolutional neural networks (CNNs) have achieved state of the art performance on recognizing and representing audio, images, videos and 3D volumes; that is, domains where the input can be characterized by a regular graph structure. However, generalizing CNNs to irregular domains like 3D meshes is challenging.Additionally, training data for 3D meshes is often limited.In this work, we generalize convolutional autoencoders to mesh surfaces.We perform spectral decomposition of meshes and apply convolutions directly in frequency space.In addition, we use max pooling and introduce upsampling within the network to represent meshes in a low dimensional space.We construct a complex dataset of 20,466 high resolution meshes with extreme facial expressions and encode it using our Convolutional Mesh Autoencoder.Despite limited training data, our method outperforms state-of-the-art PCA models of faces with 50% lower error, while using 75% fewer parameters.", "target": ["Autoencoder convoluzionali generalizzati a superfici mesh per la codifica e la ricostruzione di espressioni facciali 3D estreme."]} +{"source": "Computing distances between examples is at the core of many learning algorithms for time series.Consequently, a great deal of work has gone into designing effective time series distance measures.We present Jiffy, a simple and scalable distance metric for multivariate time series.Our approach is to reframe the task as a representation learning problem---rather than design an elaborate distance function, we use a CNN to learn an embedding such that the Euclidean distance is effective.By aggressively max-pooling and downsampling, we are able to construct this embedding using a highly compact neural network.Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods.", "target": ["Jiffy è un approccio convoluzionale all'apprendimento di una metrica di distanza per le serie temporali multivariate che supera i metodi esistenti in termini di accuratezza della classificazione nearest-neighbor."]} +{"source": "Prefrontal cortex (PFC) is a part of the brain which is responsible for behavior repertoire.Inspired by PFC functionality and connectivity, as well as human behavior formation process, we propose a novel modular architecture of neural networks with a Behavioral Module (BM) and corresponding end-to-end training strategy. This approach allows the efficient learning of behaviors and preferences representation.This property is particularly useful for user modeling (as for dialog agents) and recommendation tasks, as allows learning personalized representations of different user states. In the experiment with video games playing, the resultsshow that the proposed method allows separation of main task’s objectives andbehaviors between different BMs.The experiments also show network extendability through independent learning of new behavior patterns.Moreover, we demonstrate a strategy for an efficient transfer of newly learned BMs to unseen tasks.", "target": ["L'architettura modulare estendibile è proposta per lo sviluppo di una varietà di comportamenti di agenti in DQN."]} +{"source": "A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP).We provide a general framework for analyzing this scenario, which we use to design multiple synthetic benchmarks from only modifying the observation space of an MDP.When an agent overfits to different observation spaces even if the underlying MDP dynamics is fixed, we term this observational overfitting.Our experiments expose intriguing properties especially with regards to implicit regularization, and also corroborate results from previous works in RL generalization and supervised learning (SL).", "target": ["Isoliamo un fattore di generalizzazione RL analizzando il caso in cui l'agente si adatta solo alle osservazioni. Mostriamo che le regolarizzazioni implicite dell'architettura si verificano in questo regime."]} +{"source": "We propose a neural language model capable of unsupervised syntactic structure induction.The model leverages the structure information to form better semantic representations and better language modeling.Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic information.On the other hand, tree-structured recursive networks usually require additional structural supervision at the cost of human expert annotation.In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model.In our model, the gradient can be directly back-propagated from the language model loss into the neural parsing network.Experiments show that the proposed model can discover the underlying syntactic structure and achieve state-of-the-art performance on word/character-level language model tasks.", "target": ["In questo articolo, proponiamo un nuovo neural language model, chiamato Parsing-Reading-Predict Network (PRPN), che può indurre simultaneamente la struttura sintattica da frasi non annotate e sfruttare la struttura dedotta per imparare un language model migliore."]} +{"source": "Unsupervised embedding learning aims to extract good representations from data without the use of human-annotated labels.Such techniques are apparently in the limelight because of the challenges in collecting massive-scale labels required for supervised learning.This paper proposes a comprehensive approach, called Super-AND, which is based on the Anchor Neighbourhood Discovery model.Multiple losses defined in Super-AND make similar samples gather even within a low-density space and keep features invariant against augmentation.As a result, our model outperforms existing approaches in various benchmark datasets and achieves an accuracy of 89.2% in CIFAR-10 with the Resnet18 backbone network, a 2.9% gain over the state-of-the-art.", "target": ["Abbiamo proposto un approccio completo per l'apprendimento unsupervised di embedding sulla base dell'algoritmo AND."]} +{"source": "Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard.In the case of digital histopathological analysis, highly trained pathologists must review vast whole-slide-images of extreme digital resolution (100,000^2 pixels) across multiple zoom levels in order to locate abnormal regions of cells, or in some cases single cells, out of millions.The application of deep learning to this problem is hampered not only by small sample sizes, as typical datasets contain only a few hundred samples, but also by the generation of ground-truth localized annotations for training interpretable classification and segmentation models.We propose a method for disease available during training.Even without pixel-level annotations, we are able to demonstrate performance comparable with models trained with strong annotations on the Camelyon-16 lymph node metastases detection challenge.We accomplish this through the use of pre-trained deep convolutional networks, feature embedding, as well as learning via top instances and negative evidence, a multiple instance learning technique fromatp the field of semantic segmentation and object detection.", "target": ["Proponiamo un metodo di apprendimento weakly supervised per la classificazione e la localizzazione dei tumori in immagini istopatologiche di vetrini interi ad altissima risoluzione usando solo label a livello di immagine."]} +{"source": "Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions.One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, resulting in few positive examples for the rare labels.We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels. We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision.", "target": ["Proponiamo un nuovo metodo per utilizzare le informazioni dell'ontologia per migliorare le prestazioni su problemi di predizione/classificazione massicciamente multi-label."]} +{"source": "Generative Adversarial Networks have made data generation possible in various use cases, but in case of complex, high-dimensional distributions it can be difficult to train them, because of convergence problems and the appearance of mode collapse.Sliced Wasserstein GANs and especially the application of the Max-Sliced Wasserstein distance made it possible to approximate Wasserstein distance during training in an efficient and stable way and helped ease convergence problems of these architectures.This method transforms sample assignment and distance calculation into sorting the one-dimensional projection of the samples, which results a sufficient approximation of the high-dimensional Wasserstein distance. In this paper we will demonstrate that the approximation of the Wasserstein distance by sorting the samples is not always the optimal approach and the greedy assignment of the real and fake samples can result faster convergence and better approximation of the original distribution.", "target": ["Applichiamo un'assegnazione greedy sui sample proiettati invece del sorting per approssimare la distanza di Wasserstein"]} +{"source": "Lifelong machine learning focuses on adapting to novel tasks without forgetting the old tasks, whereas few-shot learning strives to learn a single task given a small amount of data.These two different research areas are crucial for artificial general intelligence, however, their existing studies have somehow assumed some impractical settings when training the models.For lifelong learning, the nature (or the quantity) of incoming tasks during inference time is assumed to be known at training time.As for few-shot learning, it is commonly assumed that a large number of tasks is available during training.Humans, on the other hand, can perform these learning tasks without regard to the aforementioned assumptions.Inspired by how the human brain works, we propose a novel model, called the Slow Thinking to Learn (STL), that makes sophisticated (and slightly slower) predictions by iteratively considering interactions between current and previously seen tasks at runtime.Having conducted experiments, the results empirically demonstrate the effectiveness of STL for more realistic lifelong and few-shot learning settings.", "target": ["Questo articolo studia le interazioni tra i modelli di apprendimento veloce e di predizione lenta e dimostra come tali interazioni possano migliorare la capacità della macchina di risolvere i problemi di joint lifelong e few shot learning."]} +{"source": "Hypernetworks are meta neural networks that generate weights for a main neural network in an end-to-end differentiable manner.Despite extensive applications ranging from multi-task learning to Bayesian deep learning, the problem of optimizing hypernetworks has not been studied to date.We observe that classical weight initialization methods like Glorot & Bengio (2010) and He et al. (2015), when applied directly on a hypernet, fail to produce weights for the mainnet in the correct scale.We develop principled techniques for weight initialization in hypernets, and show that they lead to more stable mainnet weights, lower training loss, and faster convergence.", "target": ["Il primo metodo di inizializzazione dei pesi basato su principi robusti per le iperreti"]} +{"source": "For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework.VHE randomized GAN (VHE-GAN) encodes an image to decode its associated text, and feeds the variational posterior as the source of randomness into the GAN image generator.We plug three off-the-shelf modules, including a deep topic model, a ladder-structured image encoder, and StackGAN++, into VHE-GAN, which already achieves competitive performance.This further motivates the development of VHE-raster-scan-GAN that generates photo-realistic images in not only a multi-scale low-to-high-resolution manner, but also a hierarchical-semantic coarse-to-fine fashion.By capturing and relating hierarchical semantic and visual concepts with end-to-end training, VHE-raster-scan-GAN achieves state-of-the-art performance in a wide variety of image-text multi-modality learning and generation tasks.", "target": ["Un nuovo framework di deep learning bayesiano che cattura e mette in relazione concetti semantici e visivi gerarchici, con buone prestazioni su una varietà di task di generazione e modellazione di immagini e testi."]} +{"source": "Current classical planners are very successful in finding (non-optimal) plans, even for large planning instances.To do so, most planners rely on a preprocessing stage that computes a grounded representation of the task.Whenever the grounded task is too big to be generated (i.e., whenever this preprocess fails) the instance cannot even be tackled by the actual planner.To address this issue, we introduce a partial grounding approach that grounds only a projection of the task, when complete grounding is not feasible.We propose a guiding mechanism that, for a given domain, identifies the parts of a task that are relevant to find a plan by using off-the-shelf machine learning methods.Our empirical evaluation attests that the approach is capable of solving planning instances that are too big to be fully grounded.", "target": ["Questo articolo introduce il grounding parziale per affrontare il problema che si presenta quando il processo di grounding completo, cioè la traduzione di un task di input PDDL in una ground representation come STRIPS, non è fattibile a causa di limiti di memoria o di tempo."]} +{"source": "In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level.We propose a systematic pipeline for interpreting individual hidden state dynamics within the network using response characterization methods.The ranked contribution of individual cells to the network's output is computed by analyzing a set of interpretable metrics of their decoupled step and sinusoidal responses.As a result, our method is able to uniquely identify neurons with insightful dynamics, quantify relationships between dynamical properties and test accuracy through ablation analysis, and interpret the impact of network capacity on a network's dynamical distribution.Finally, we demonstrate generalizability and scalability of our method by evaluating a series of different benchmark sequential datasets.", "target": ["Introduzione del metodo di caratterizzazione della risposta per interpretare la dinamica delle cellule nelle reti LSTM addestrate."]} +{"source": "Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties.The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns.However, the mechanisms and functional significance of these spatial representations remain largely mysterious.As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs.Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells.All these different functional types of neurons have been observed experimentally.The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies.Together, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits.", "target": ["A nostra conoscenza, questo è il primo studio che mostra come le rappresentazioni neurali dello spazio, comprese le cellule a griglia e le cellule border osservate nel cervello, potrebbero emergere dal training di una rete neurale ricorrente per eseguire task di navigazione."]} +{"source": "Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song.Such components include voice, bass, drums and any other accompaniments.While end-to-end models that directly generate the waveform are state-of-the-art in many audio synthesis problems, the best multi-instrument source separation models generate masks on the magnitude spectrum and achieve performances far above current end-to-end, waveform-to-waveform models.We present an in-depth analysis of a new architecture, which we will refer to as Demucs, based on a (transposed) convolutional autoencoder, with a bidirectional LSTM at the bottleneck layer and skip-connections as in U-Networks (Ronneberger et al., 2015).Compared to the state-of-the-art waveform-to-waveform model, Wave-U-Net (Stoller et al., 2018), the main features of our approach in addition of the bi-LSTM are the use of trans-posed convolution layers instead of upsampling-convolution blocks, the use of gated linear units, exponentially growing the number of channels with depth and a new careful initialization of the weights. Results on the MusDB dataset show that our architecture achieves a signal-to-distortion ratio (SDR) nearly 2.2 points higher than the best waveform-to-waveform competitor (from 3.2 to 5.4 SDR).This makes our model match the state-of-the-art performances on this dataset, bridging the performance gap between models that operate on the spectrogram and end-to-end approaches.", "target": ["Confrontiamo le prestazioni del modello basato sullo spettrogramma con un modello addestrato end-to-end nel dominio della forma d'onda"]} +{"source": "Although challenging, strategy profile evaluation in large connected learner networks is crucial for enabling the next wave of machine learning applications.Recently, $\\alpha$-Rank, an evolutionary algorithm, has been proposed as a solution for ranking joint policy profiles in multi-agent systems.$\\alpha$-Rank claimed scalability through a polynomial time implementation with respect to the total number of pure strategy profiles.In this paper, we formally prove that such a claim is not grounded.In fact, we show that $\\alpha$-Rank exhibits an exponential complexity in number of agents, hindering its application beyond a small finite number of joint profiles.Realizing such a limitation, we contribute by proposing a scalable evaluation protocol that we title $\\alpha^{\\alpha}$-Rank.Our method combines evolutionary dynamics with stochastic optimization and double oracles for \\emph{truly} scalable ranking with linear (in number of agents) time and memory complexities.Our contributions allow us, for the first time, to conduct large-scale evaluation experiments of multi-agent systems, where we show successful results on large joint strategy profiles with sizes in the order of $\\mathcal{O}(2^{25})$ (i.e., $\\approx \\text{$33$ million strategies}$) -- a setting not evaluable using current techniques.", "target": ["Forniamo una soluzione scalabile per la valutazione multi-agente con una complessità lineare sia nel tempo che nella memoria in termini di numero di agenti"]} +{"source": "Deep neural networks are known to be annotation-hungry.Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks.Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data.In this work, we propose DivideMix, a novel framework for learning with noisy labels by leveraging semi-supervised learning techniques.In particular, DivideMix models the per-sample loss distribution with a mixture model to dynamically divide the training data into a labeled set with clean samples and an unlabeled set with noisy samples, and trains the model on both the labeled and unlabeled data in a semi-supervised manner.To avoid confirmation bias, we simultaneously train two diverged networks where each network uses the dataset division from the other network.During the semi-supervised training phase, we improve the MixMatch strategy by performing label co-refinement and label co-guessing on labeled and unlabeled samples, respectively.Experiments on multiple benchmark datasets demonstrate substantial improvements over state-of-the-art methods.Code is available at https://github.com/LiJunnan1992/DivideMix .", "target": ["Proponiamo un nuovo approccio di apprendimento semi-supervised con prestazioni SOTA per combattere l'apprendimento con label rumorose."]} +{"source": "We present a new algorithm to train a robust neural network against adversarial attacks. Our algorithm is motivated by the following two ideas.First, although recent work has demonstrated that fusing randomness can improve the robustness of neural networks (Liu 2017), we noticed that adding noise blindly to all the layers is not the optimal way to incorporate randomness. Instead, we model randomness under the framework of Bayesian Neural Network (BNN) to formally learn the posterior distribution of models in a scalable way.Second, we formulate the mini-max problem in BNN to learn the best model distribution under adversarial attacks, leading to an adversarial-trained Bayesian neural net.Experiment results demonstrate that the proposed algorithm achieves state-of-the-art performance under strong attacks.On CIFAR-10 with VGG network, our model leads to 14% accuracy improvement compared with adversarial training (Madry 2017) and random self-ensemble (Liu, 2017) under PGD attack with 0.035 distortion, and the gap becomes even larger on a subset of ImageNet.", "target": ["Progettiamo un metodo di adversarial training per le reti neurali bayesiane, mostrando una difesa molto più forte agli adversarial attack white-box"]} +{"source": "Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server.In this work, we explore the threat of model poisoning attacks on federated learning initiated by a single, non-colluding malicious agent where the adversarial objective is to cause the model to misclassify a set of chosen inputs with high confidence.We explore a number of strategies to carry out this attack, starting with simple boosting of the malicious agent's update to overcome the effects of other agents' updates.To increase attack stealth, we propose an alternating minimization strategy, which alternately optimizes for the training loss and the adversarial objective.We follow up by using parameter estimation for the benign agents' updates to improve on attack success.Finally, we use a suite of interpretability techniques to generate visual explanations of model decisions for both benign and malicious models and show that the explanations are nearly visually indistinguishable.Our results indicate that even a highly constrained adversary can carry out model poisoning attacks while simultaneously maintaining stealth, thus highlighting the vulnerability of the federated learning setting and the need to develop effective defense strategies.", "target": ["Efficaci attacchi di model poisoning al federated learning sono in grado di causare un'errata classificazione ad alta confidenza mirata agli input desiderati"]} +{"source": "Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs.Small amounts of noise can destroy the performance of an otherwise state-of-the-art model.To harden models against background noise, practitioners often perform data augmentation, adding artificially-noised examples to the training set, carrying over the original label.In this paper, we hypothesize that a clean example and its superficially perturbed counterparts shouldn't merely map to the same class--- they should map to the same representation.We propose invariant-representation-learning (IRL): At each training iteration, for each training example, we sample a noisy counterpart.We then apply a penalty term to coerce matched representations at each layer (above some chosen layer).Our key results, demonstrated on the LibriSpeech dataset are the following:(i) IRL significantly reduces character error rates (CER)on both `clean' (3.3% vs 6.5%) and `other' (11.0% vs 18.1%) test sets;(ii) on several out-of-domain noise settings (different from those seen during training), IRL's benefits are even more pronounced.Careful ablations confirm that our results are not simply due to shrinking activations at the chosen layers.", "target": ["In questo articolo, ipotizziamo che i data point superficialmente perturbati non dovrebbero semplicemente essere mappati alla stessa classe - dovrebbero essere mappati alla stessa rappresentazione."]} +{"source": "In this paper we design a harmonic acoustic model for pitch detection.This model arranges conventional convolution and sparse convolution in a way such that the global harmonic patterns captured by sparse convolution are composed of the enough number of local patterns captured by layers of conventional convolution.When trained on the MAPS dataset, the harmonic model outperforms all existing pitch detection systems trained on the same dataset.Most impressively, when trained on MAPS with simple data augmentation, the harmonic model with an LSTM layer on top surpasses an up-to-date, more complex pitch detection system trained on the MAESTRO dataset to which complicated data augmentation is applied and whose training split is an order-of-magnitude larger than the training split of MAPS.The harmonic model has demonstrated potential to be used for advanced automatic music transcription (AMT) systems.", "target": ["modello acustico armonico"]} +{"source": "Learning domain-invariant representation is a dominant approach for domain generalization.However, previous methods based on domain invariance overlooked the underlying dependency of classes on domains, which is responsible for the trade-off between classification accuracy and the invariance.This study proposes a novel method {\\em adversarial feature learning under accuracy constraint (AFLAC)}, which maximizes domain invariance within a range that does not interfere with accuracy.Empirical validations show that the performance of AFLAC is superior to that of baseline methods, supporting the importance of considering the dependency and the efficacy of the proposed method to overcome the problem.", "target": ["Affrontare il trade-off causato dalla dipendenza delle classi dai domini migliorando le reti adversarial di dominio"]} +{"source": "Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images.Following their successful application in image processing and representation learning, an important next step is to consider videos.Learning generative models of video is a much harder task, requiring a model to capture the temporal dynamics of a scene, in addition to the visual presentation of objects.While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples.To this extent we propose Fréchet Video Distance (FVD), a new metric for generative models of video based on FID.We contribute a large-scale human study, which confirms that FVD correlates well with qualitative human judgment of generated videos.", "target": ["Proponiamo FVD: una nuova metrica per modelli generativi di video basati su FID. Uno studio umano su larga scala conferma che FVD è ben correlato con il giudizio umano sulla qualità dei video generati."]} +{"source": "Despite advances in deep learning, artificial neural networks do not learn the same way as humans do.Today, neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on learnt tasks when tasks are presented one at a time -- this phenomenon called catastrophic forgetting is a fundamental challenge to overcome before neural networks can learn continually from incoming data.In this work, we derive inspiration from human memory to develop an architecture capable of learning continuously from sequentially incoming tasks, while averting catastrophic forgetting.Specifically, our model consists of a dual memory architecture to emulate the complementary learning systems (hippocampus and the neocortex) in the human brain and maintains a consolidated long-term memory via generative replay of past experiences.We(i) substantiate our claim that replay should be generative,(ii) show the benefits of generative replay and dual memory via experiments, and(iii) demonstrate improved performance retention even for small models with low capacity.Our architecture displays many important characteristics of the human memory and provides insights on the connection between sleep and learning in humans.", "target": ["Un'architettura di memoria doppia ispirata al cervello umano per imparare in modo sequenziale i task in arrivo, evitando la catastrophic forgetting."]} +{"source": "Most research on lifelong learning applies to images or games, but not language.We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling.LAMOL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity.Specifically, LAMOL is a language model that simultaneously learns to solve the tasks and generate training samples.When the model is trained for a new task, it generates pseudo-samples of previous tasks for training alongside data for the new task.The results show that LAMOL prevents catastrophic forgetting without any sign of intransigence and can perform five very different language tasks sequentially with only one model. Overall, LAMOL outperforms previous methods by a considerable margin and is only 2-3% worse than multitasking, which is usually considered the LLL upper bound.The source code is available at https://github.com/jojotenya/LAMOL.", "target": ["Language modeling per lifelong language learning."]} +{"source": "Deep learning natural language processing models often use vector word embeddings, such as word2vec or GloVe, to represent words.A discrete sequence of words can be much more easily integrated with downstream neural layers if it is represented as a sequence of continuous vectors.Also, semantic relationships between words, learned from a text corpus, can be encoded in the relative configurations of the embedding vectors.However, storing and accessing embedding vectors for all words in a dictionary requires large amount of space, and may stain systems with limited GPU memory.Here, we used approaches inspired by quantum computing to propose two related methods, word2ket and word2ketXS, for storing word embedding matrix during training and inference in a highly efficient way.Our approach achieves a hundred-fold or more reduction in the space required to store the embeddings with almost no relative drop in accuracy in practical natural language processing tasks.", "target": ["Usiamo idee dal quantum computing per proporre word embedding che utilizzano molti meno parametri learnable."]} +{"source": "One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary.Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.g. hand- engineered features).Humans, however, do not learn to communicate based on well-summarized features.In this work, we train neural agents to simultaneously develop visual perception from raw image pixels, and learn to communicate with a sequence of discrete symbols.The agents play an image description game where the image contains factors such as colors and shapes.We train the agents using the obverter technique where an agent introspects to generate messages that maximize its own understanding.Through qualitative analysis, visualization and a zero-shot test, we show that the agents can develop, out of raw image pixels, a language with compositional properties, given a proper pressure from the environment.", "target": ["Addestriamo agenti di rete neurale per sviluppare un linguaggio con proprietà compositive da input di pixel grezzi."]} +{"source": "The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles).In particular, a set of possible future behaviors generated by the system must be diverse to account for all possible outcomes in order to take necessary safety precautions.It is not sufficient to maintain a set of the most likely future outcomes because the set may only contain perturbations of a dominating single outcome (major mode).While generative models such as variational autoencoders (VAEs) have been shown to be a powerful tool for learning a distribution over future trajectories, randomly drawn samples from the learned implicit likelihood model may not be diverse -- the likelihood model is derived from the training data distribution and the samples will concentrate around the major mode of the data.In this work, we propose to learn a diversity sampling function (DSF) that generates a diverse yet likely set of future trajectories.The DSF maps forecasting context features to a set of latent codes which can be decoded by a generative model (e.g., VAE) into a set of diverse trajectory samples.Concretely, the process of identifying the diverse set of samples is posed as DSF parameter estimation.To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP).Gradient descent is performed over the DSF parameters, which in turn moves the latent codes of the sample set to find an optimal set of diverse yet likely trajectories.Our method is a novel application of DPPs to optimize a set of items (forecasted trajectories) in continuous space.We demonstrate the diversity of the trajectories produced by our approach on both low-dimensional 2D trajectory data and high-dimensional human motion data.", "target": ["Impariamo una funzione di diversity sampling con le DPP per ottenere un insieme diversificato di sample da un modello generativo."]} +{"source": "There is mounting evidence that pretraining can be valuable for neural network language understanding models, but we do not yet have a clear understanding of how the choice of pretraining objective affects the type of linguistic information that models learn.With this in mind, we compare four objectives---language modeling, translation, skip-thought, and autoencoding---on their ability to induce syntactic and part-of-speech information, holding constant the genre and quantity of training data.We find that representations from language models consistently perform best on our syntactic auxiliary prediction tasks, even when trained on relatively small amounts of data, which suggests that language modeling may be the best data-rich pretraining task for transfer learning applications requiring syntactic information.We also find that a randomly-initialized, frozen model can perform strikingly well on our auxiliary tasks, but that this effect disappears when the amount of training data for the auxiliary tasks is reduced.", "target": ["Le rappresentazioni dei language model hanno immancabilmente prestazioni migliori degli encoder di traduzione nei task di syntactic auxiliary prediction."]} +{"source": "We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied. Consider, for example, the trade-off between thermal resistance, electrical conductivity, and mechanical stability needed to design a nano-porous template with optimal thermoelectric efficiency. To that end, we leverage the posterior regularization framework andshow that this constraint satisfaction problem can be formulated as sampling froma Gibbs distribution. The main challenges come from the black-box nature ofthose physical constraints, since they are obtained via solving highly non-linearPDEs.To overcome those difficulties, we introduce Surrogate-based Constrained Langevin dynamics for black-box sampling.We explore two surrogate approaches.The first approach exploits zero-order approximation of gradients in the Langevin Sampling and we refer to it as Zero-Order Langevin.In practice, this approach can be prohibitive since we still need to often query the expensive PDE solvers.The second approach approximates the gradients in the Langevin dynamics with deep neural networks, allowing us an efficient sampling strategy using the surrogate model.We prove the convergence of those two approaches when the target distribution is log-concave and smooth.We show the effectiveness of both approaches in designing optimal nano-porous material configurations, where the goal is to produce nano-pattern templates with low thermal conductivity and reasonable mechanical stability.", "target": ["Proponiamo il sampling di Langevin vincolato basato su surrogati con applicazione nella progettazione della configurazione di materiali nano-porosi."]} +{"source": "There is growing interest in geometrically-inspired embeddings for learning hierarchies, partial orders, and lattice structures, with natural applications to transitive relational data such as entailment graphs.Recent work has extended these ideas beyond deterministic hierarchies to probabilistically calibrated models, which enable learning from uncertain supervision and inferring soft-inclusions among concepts, while maintaining the geometric inductive bias of hierarchical embedding models.We build on the Box Lattice model of Vilnis et al. (2018), which showed promising results in modeling soft-inclusions through an overlapping hierarchy of sets, parameterized as high-dimensional hyperrectangles (boxes).However, the hard edges of the boxes present difficulties for standard gradient based optimization; that work employed a special surrogate function for the disjoint case, but we find this method to be fragile. In this work, we present a novel hierarchical embedding model, inspired by a relaxation of box embeddings into parameterized density functions using Gaussian convolutions over the boxes.Our approach provides an alternative surrogate to the original lattice measure that improves the robustness of optimization in the disjoint case, while also preserving the desirable properties with respect to the original lattice.We demonstrate increased or matching performance on WordNet hypernymy prediction, Flickr caption entailment, and a MovieLens-based market basket dataset.We show especially marked improvements in the case of sparse data, where many conditional probabilities should be low, and thus boxes should be nearly disjoint.", "target": ["Migliorare i modelli di embedding gerarchico usando lo smoothing del kernel"]} +{"source": "We present a weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities (e.g., proteins) from the scientific literature.First, we train a neural NER (NNER) model over a small seed of fully-labeled examples.Second, we use a reference set of entity names (e.g., proteins in UniProt) to identify entity mentions with high precision, but low recall, on an unlabeled corpus.Third, we use the NNER model to assign weak labels to the corpus.Finally, we retrain our NNER model iteratively over the augmented training set, including the seed, the reference-set examples, and the weakly-labeled examples, which results in refined labels.We show empirically that this augmented bootstrapping process significantly improves NER performance, and discuss the factors impacting the efficacy of the approach.", "target": ["Approccio di bootstrapping aumentato che combina informazioni da un set di riferimento con raffinamenti iterativi di label soft per migliorare il name entity recognition dalla letteratura biomedica."]} +{"source": "Quantum machine learning methods have the potential to facilitate learning using extremely large datasets.While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels.One of the approaches for addressing this issue is to use semi-supervised learning, which leverages not only the labeled samples, but also unlabeled feature vectors.Here, we present a quantum machine learning algorithm for training Semi-Supervised Kernel Support Vector Machines.The algorithm uses recent advances in quantum sample-based Hamiltonian simulation to extend the existing Quantum LS-SVM algorithm to handle the semi-supervised term in the loss, while maintaining the same quantum speedup as the Quantum LS-SVM.", "target": ["Estendiamo i quantum SVM al setting semi-supervisionato, per affrontare il frequente problema di molte label di classe mancanti in enormi dataset."]} +{"source": "Deep neural networks have become the state-of-the-art models in numerous machine learning tasks.However, general guidance to network architecture design is still missing.In our work, we bridge deep neural network design with numerical differential equations.We show that many effective networks, such as ResNet, PolyNet, FractalNet and RevNet, can be interpreted as different numerical discretizations of differential equations.This finding brings us a brand new perspective on the design of effective deep architectures.We can take advantage of the rich knowledge in numerical analysis to guide us in designing new and potentially more effective deep networks.As an example, we propose a linear multi-step architecture (LM-architecture) which is inspired by the linear multi-step method solving ordinary differential equations.The LM-architecture is an effective structure that can be used on any ResNet-like networks.In particular, we demonstrate that LM-ResNet and LM-ResNeXt (i.e. the networks obtained by applying the LM-architecture on ResNet and ResNeXt respectively) can achieve noticeably higher accuracy than ResNet and ResNeXt on both CIFAR and ImageNet with comparable numbers of trainable parameters.In particular, on both CIFAR and ImageNet, LM-ResNet/LM-ResNeXt can significantly compress (>50%) the original networks while maintaining a similar performance.This can be explained mathematically using the concept of modified equation from numerical analysis.Last but not least, we also establish a connection between stochastic control and noise injection in the training process which helps to improve generalization of the networks.Furthermore, by relating stochastic training strategy with stochastic dynamic system, we can easily apply stochastic training to the networks with the LM-architecture.As an example, we introduced stochastic depth to LM-ResNet and achieve significant improvement over the original LM-ResNet on CIFAR10.", "target": ["Questo articolo collega le architetture delle deep network con le equazioni differenziali numeriche (stocastiche). Questa nuova prospettiva permette nuovi design di deep neural network più efficaci."]} +{"source": "Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications.In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies).", "target": ["CNN e LSTM per generare codice simile al markup che descrive le immagini dell'interfaccia grafica."]} +{"source": "Computer vision tasks such as image classification, image retrieval and few-shot learning are currently dominated by Euclidean and spherical embeddings, so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances (cosine similarity).In this work, we demonstrate that in many practical scenarios hyperbolic embeddings provide a better alternative.", "target": ["Mostriamo che gli embedding iperbolici sono utili per task di visione di alto livello, specialmente per la classificazione few shot."]} +{"source": "High-dimensional time series are common in many domains.Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations.However, most representation learning algorithms for time series data are difficult to interpret.This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time.To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling.This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance.We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original.Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space.This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty.We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.", "target": ["Presentiamo un metodo per imparare rappresentazioni interpretabili su serie temporali usando idee da autoencoder variazionali, mappe auto-organizzanti e modelli probabilistici."]} +{"source": "We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of adjusted regressors, while the weights are data-dependent functions learnt through a convolutional network.The architecture was designed for applications on asynchronous time series and is evaluated on such datasets: a hedge fund proprietary dataset of over 2 million quotes for a credit derivative index, an artificially generated noisy autoregressive series and household electricity consumption dataset. The pro-posed architecture achieves promising results as compared to convolutional and recurrent neural networks.The code for the numerical experiments and the architecture implementation will be shared online to make the research reproducible.", "target": ["Architettura convoluzionale per l'apprendimento di pesi dipendenti dai dati per la predizione autoregressiva di serie temporali."]} +{"source": "MixUp is a data augmentation scheme in which pairs of training samples and their corresponding labels are mixed using linear coefficients.Without label mixing, MixUp becomes a more conventional scheme: input samples are moved but their original labels are retained.Because samples are preferentially moved in the direction of other classes \\iffalse -- which are typically clustered in input space -- \\fi we refer to this method as directional adversarial training, or DAT.We show that under two mild conditions, MixUp asymptotically convergences to a subset of DAT.We define untied MixUp (UMixUp), a superset of MixUp wherein training labels are mixed with different linear coefficients to those of their corresponding samples.We show that under the same mild conditions, untied MixUp converges to the entire class of DAT schemes.Motivated by the understanding that UMixUp is both a generalization of MixUp and a form of adversarial training, we experiment with different datasets and loss functions to show that UMixUp provides improved performance over MixUp.In short, we present a novel interpretation of MixUp as belonging to a class highly analogous to adversarial training, and on this basis we introduce a simple generalization which outperforms MixUp.", "target": ["Presentiamo una nuova interpretazione di MixUp come appartenente a una classe altamente analoga all'adversarial training, e su questa base introduciamo una semplice generalizzazione che supera MixUp"]} +{"source": "Plan recognition aims to look for target plans to best explain the observed actions based on plan libraries and/or domain models.Despite the success of previous approaches on plan recognition, they mostly rely on correct action observations. Recent advances in visual activity recognition have the potential of enabling applications such as automated video surveillance.Effective approaches for such problems would require the ability to recognize the plans of agents from video information.Traditional plan recognition algorithms rely on access to detailed planning domain models.One recent promising direction involves learning approximate (or shallow) domain models directly from the observed activity sequences.Such plan recognition approaches expect observed action sequences as inputs.However, visual inference results are often noisy and uncertain, typically represented as a distribution over possible actions.In this work, we develop a visual plan recognition framework that recognizes plans with an approximate domain model learned from uncertain visual data.", "target": ["Gestire l'incertezza nella percezione visiva per plan recognition"]} +{"source": "We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB).In particular, we describe a neural module, DrKIT, that traverses textual data like a virtual KB, softly following paths of relations between mentions of entities in the corpus.At each step the operation uses a combination of sparse-matrix TFIDF indices and maximum inner product search (MIPS) on a special index of contextual representations.This module is differentiable, so the full system can be trained completely end-to-end using gradient based methods, starting from natural language inputs.We also describe a pretraining scheme for the index mention encoder by generating hard negative examples using existing knowledge bases.We show that DrKIT improves accuracy by 9 points on 3-hop questions in the MetaQA dataset, cutting the gap between text-based and KB-based state-of-the-art by 70%.DrKIT is also very efficient, processing upto 10x more queries per second than existing state-of-the-art QA systems.", "target": ["Accesso multi-hop differenziabile a una knowledge base testuale di rappresentazioni contestuali indicizzate"]} +{"source": "In spite of their great success, traditional factorization algorithms typically do not support features (e.g., Matrix Factorization), or their complexity scales quadratically with the number of features (e.g, Factorization Machine).On the other hand, neural methods allow large feature sets, but are often designed for a specific application.We propose novel deep factorization methods that allow efficient and flexible feature representation.For example, we enable describing items with natural language with complexity linear to the vocabulary size—this enables prediction for unseen items and avoids the cold start problem.We show that our architecture can generalize some previously published single-purpose neural architectures.Our experiments suggest improved training times and accuracy compared to shallow methods.", "target": ["Algoritmo di fattorizzazione generale scalabile - aiuta anche ad aggirare il problema dell'avvio a freddo."]} +{"source": "Augmented Reality (AR) can assist with physical tasks such as object assembly through the use of “situated instructions”.These instructions can be in the form of videos, pictures, text or guiding animations, where the most helpful media among these is highly dependent on both the user and the nature of the task.Our work supports the authoring of AR tutorials for assembly tasks with little overhead beyond simply performing the task itself.The presented system, AuthAR reduces the time and effort required to build interactive AR tutorials by automatically generating key components of the AR tutorial while the author is assembling the physical pieces.Further, the system guides authors through the process of adding videos, pictures, text and animations to the tutorial.This concurrent assembly and tutorial generation approach allows for authoring of portable tutorials that fit the preferences of different end users.", "target": ["Presentiamo un sistema di authoring di tutorial di montaggio a media misti che ottimizza la creazione di video, immagini, testo e istruzioni dinamiche in situ."]} +{"source": "Monitoring patients in ICU is a challenging and high-cost task.Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources.There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments.In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management.While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together.We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model.", "target": ["Dimostriamo che l'uso delle note cliniche in congiunzione con i dati degli strumenti dell'ICU migliora la performance nei task di benchmark della gestione dell'ICU"]} +{"source": "Existing works in deep Multi-Agent Reinforcement Learning (MARL) mainly focus on coordinating cooperative agents to complete certain tasks jointly.However, in many cases of the real world, agents are self-interested such as employees in a company and clubs in a league.Therefore, the leader, i.e., the manager of the company or the league, needs to provide bonuses to followers for efficient coordination, which we call expensive coordination.The main difficulties of expensive coordination are thati) the leader has to consider the long-term effect and predict the followers' behaviors when assigning bonuses andii) the complex interactions between followers make the training process hard to converge, especially when the leader's policy changes with time.In this work, we address this problem through an event-based deep RL approach.Our main contributions are threefold.(1) We model the leader's decision-making process as a semi-Markov Decision Process and propose a novel multi-agent event-based policy gradient to learn the leader's long-term policy.(2) We exploit the leader-follower consistency scheme to design a follower-aware module and a follower-specific attention module to predict the followers' behaviors and make accurate response to their behaviors.(3) We propose an action abstraction-based policy gradient algorithm to reduce the followers' decision space and thus accelerate the training process of followers.Experiments in resource collections, navigation, and the predator-prey game reveal that our approach outperforms the state-of-the-art methods dramatically.", "target": ["Proponiamo un gradiente di policy basato sugli eventi per addestrare il leader e un gradiente di policy di astrazione dell'azione per addestrare i follower nel gioco di Markov leader-follower."]} +{"source": "Recent work has studied the emergence of language among deep reinforcement learning agents that must collaborate to solve a task.Of particular interest are the factors that cause language to be compositional---i.e., express meaning by combining words which themselves have meaning.Evolutionary linguists have found that in addition to structural priors like those already studied in deep learning, the dynamics of transmitting language from generation to generation contribute significantly to the emergence of compositionality.In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language.We show that this implicit cultural transmission encourages the resulting languages to exhibit better compositional generalization.", "target": ["Usiamo la trasmissione culturale per incoraggiare la compositività nei linguaggi che emergono dalle interazioni tra agenti neurali."]} +{"source": "Based on our observation that there exists a dramatic drop for the singular values of the fully connected layers or a single feature map of the convolutional layer, and that the dimension of the concatenated feature vector almost equals the summation of the dimension on each feature map, we propose a singular value decomposition (SVD) based approach to estimate the dimension of the deep manifolds for a typical convolutional neural network VGG19.We choose three categories from the ImageNet, namely Persian Cat, Container Ship and Volcano, and determine the local dimension of the deep manifolds of the deep layers through the tangent space of a target image.Through several augmentation methods, we found that the Gaussian noise method is closer to the intrinsic dimension, as by adding random noise to an image we are moving in an arbitrary dimension, and when the rank of the feature matrix of the augmented images does not increase we are very closeto the local dimension of the manifold.We also estimate the dimension of the deep manifold based on the tangent space for each of the maxpooling layers.Our results show that the dimensions of different categories are close to each other and decline quickly along the convolutional layers and fully connected layers.Furthermore, we show that the dimensions decline quickly inside the Conv5 layer.Our work provides new insights for the intrinsic structure of deep neural networks and helps unveiling the inner organization of the black box of deep neural networks.", "target": ["Proponiamo un metodo basato su SVD per esplorare la dimensione locale del manifold di attivazione nelle deep neural network."]} +{"source": "Large pre-trained Transformers such as BERT have been tremendously effective for many NLP tasks. However, inference in these large-capacity models is prohibitively slow and expensive. Transformers are essentially a stack of self-attention layers which encode each input position using the entire input sequence as its context. However, we find that it may not be necessary to apply this expensive sequence-wide self-attention over at all layers. Based on this observation, we propose a decomposition to a pre-trained Transformer that allows the lower layers to process segments of the input independently enabling parallelism and caching. We show that the information loss due to this decomposition can be recovered in the upper layers with auxiliary supervision during fine-tuning. We evaluate de-composition with pre-trained BERT models on five different paired-input tasks in question answering, sentence similarity, and natural language inference. Results show that decomposition enables faster inference (up to 4x), significant memory reduction (up to 70%) while retaining most (up to 99%) of the original performance. We will release the code at.", "target": ["L'inferenza in grandi trasformer è costosa a causa della self-attentionin più layer. Mostriamo che una semplice tecnica di decomposizione può produrre un modello più veloce, a bassa impronta di memoria che è altrettanto accurato rispetto ai modelli originali."]} +{"source": "Exploration while learning representations is one of the main challenges DeepReinforcement Learning (DRL) faces today.As the learned representation is dependant in the observed data, the exploration strategy has a crucial role.The popular DQN algorithm has improved significantly the capabilities of ReinforcementLearning (RL) algorithms to learn state representations from raw data, yet, it usesa naive exploration strategy which is statistically inefficient.The RandomizedLeast Squares Value Iteration (RLSVI) algorithm (Osband et al., 2016), on theother hand, explores and generalizes efficiently via linearly parameterized valuefunctions.However, it is based on hand-designed state representation that requiresprior engineering work for every environment.In this paper, we propose a DeepLearning adaptation for RLSVI.Rather than using hand-design state representation, we use a state representation that is being learned directly from the data by aDQN agent.As the representation is being optimized during the learning process,a key component for the suggested method is a likelihood matching mechanism,which adapts to the changing representations.We demonstrate the importance ofthe various properties of our algorithm on a toy problem and show that our methodoutperforms DQN in five Atari benchmarks, reaching competitive results with theRainbow algorithm.", "target": ["Un adattamento con deep learning dell'iterazione del valore dei minimi quadrati randomizzata"]} +{"source": "The complexity of large-scale neural networks can lead to poor understanding of their internal details.We show that this opaqueness provides an opportunity for adversaries to embed unintended functionalities into the network in the form of Trojan horse attacks.Our novel framework hides the existence of a malicious network within a benign transport network.Our attack is flexible, easy to execute, and difficult to detect.We prove theoretically that the malicious network's detection is computationally infeasible and demonstrate empirically that the transport network does not compromise its disguise.Our attack exposes an important, previously unknown loophole that unveils a new direction in machine learning security.", "target": ["I parametri di una rete neurale addestrata possono essere permutati per produrre un modello completamente separato per un task diverso, permettendo l'embedding di reti a cavallo di Troia dentro un'altra rete."]} +{"source": "In this paper, we introduce Random Path Generative Adversarial Network (RPGAN) --- an alternative scheme of GANs that can serve as a tool for generative model analysis.While the latent space of a typical GAN consists of input vectors, randomly sampled from the standard Gaussian distribution, the latent space of RPGAN consists of random paths in a generator network.As we show, this design allows to associate different layers of the generator with different regions of the latent space, providing their natural interpretability.With experiments on standard benchmarks, we demonstrate that RPGAN reveals several interesting insights about roles that different layers play in the image generation process.Aside from interpretability, the RPGAN model also provides competitive generation quality and allows efficient incremental learning on new data.", "target": ["Introduciamo un design GAN alternativo basato su percorsi casuali nel generatore, che può servire come strumento per l'interpretabilità dei modelli generativi."]} +{"source": "Deep artificial neural networks can achieve an extremely small difference between training and test accuracies on identically distributed training and test sets, which is a standard measure of generalization.However, the training and test sets may not be sufficiently representative of the empirical sample set, which consists of real-world input samples.When samples are drawn from an underrepresented or unrepresented subset during inference, the gap between the training and inference accuracies can be significant.To address this problem, we first reformulate a classification algorithm as a procedure for searching for a source code that maps input features to classes.We then derive a necessary and sufficient condition for generalization using a universal cognitive similarity metric, namely information distance, based on Kolmogorov complexity.Using this condition, we formulate an optimization problem to learn a more general classification function.To achieve this end, we extend the input features by concatenating encodings of them, and then train the classifier on the extended features.As an illustration of this idea, we focus on image classification, where we use channel codes on the input features as a systematic way to improve the degree to which the training and test sets are representative of the empirical sample set.To showcase our theoretical findings, considering that corrupted or perturbed input features belong to the empirical sample set, but typically not to the training and test sets, we demonstrate through extensive systematic experiments that, as a result of learning a more general classification function, a model trained on encoded input features is significantly more robust to common corruptions, e.g., Gaussian and shot noise, as well as adversarial perturbations, e.g., those found via projected gradient descent, than the model trained on uncoded input features.", "target": ["Presentiamo un framework teorico e sperimentale per definire, comprendere e raggiungere la generalizzazione, e di conseguenza la robustezza, nel deep learning attingendo alla teoria dell'informazione algoritmica e alla teoria della codifica."]} +{"source": "Many approaches to causal discovery are limited by their inability to discriminate between Markov equivalent graphs given only observational data.We formulate causal discovery as a marginal likelihood based Bayesian model selection problem.We adopt a parameterization based on the notion of the independence of causal mechanisms which renders Markov equivalent graphs distinguishable.We complement this with an empirical Bayesian approach to setting priors so that the actual underlying causal graph is assigned a higher marginal likelihood than its alternatives.Adopting a Bayesian approach also allows for straightforward modeling of unobserved confounding variables, for which we provide a variational algorithm to approximate the marginal likelihood, since this desirable feat renders the computation of the marginal likelihood intractable.We believe that the Bayesian approach to causal discovery both allows the rich methodology of Bayesian inference to be used in various difficult aspects of this problem and provides a unifying framework to causal discovery research.We demonstrate promising results in experiments conducted on real data, supporting our modeling approach and our inference methodology.", "target": ["Abbiamo lanciato la scoperta della struttura causale come una selezione di modelli bayesiani in un modo che ci permette di discriminare tra i grafi equivalenti di Markov per identificare il grafo causale unico."]} +{"source": "The goal of compressed sensing is to learn a structured signal $x$ from a limited number of noisy linear measurements $y\\approx Ax$. In traditional compressed sensing, ``structure'' is represented by sparsity in some known basis. Inspired by the success of deep learning in modeling images, recent work starting with~\\cite{BDJP17} has instead considered structure to come from a generative model $G: \\R^k \\to \\R^n$. We present two results establishing the difficulty of this latter task, showing that existing bounds are tight. First, we provide a lower bound matching the~\\cite{BDJP17} upper bound for compressed sensing from $L$-Lipschitz generative models $G$. In particular, there exists such a function that requires roughly $\\Omega(k \\log L)$ linear measurements for sparse recovery to be possible. This holds even for the more relaxed goal of \\emph{nonuniform} recovery. Second, we show that generative models generalize sparsity as a representation of structure. In particular, we construct a ReLU-based neural network $G: \\R^{2k} \\to \\R^n$ with $O(1)$ layers and $O(kn)$ activations per layer, such that the range of $G$ contains all $k$-sparse vectors.", "target": ["Lower bound per il rilevamento compresso con modelli generativi che corrisponde agli upper bound conosciuti"]} +{"source": "We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn ‘distributional similarity’ in a multimodal feature space, by mapping a test image to similar training images in this space and generating a caption from the same space.To validate our hypothesis, we focus on the ‘image’ side of image captioning, and vary the input image representation but keep the RNN text generation model of a CNN-RNN constant.We propose a sparse bag-of-objects vector as an interpretable representation to investigate our distributional similarity hypothesis.We found that image captioning models(i) are capable of separating structure from noisy input representations;(ii) experience virtually no significant performance loss when a high dimensional representation is compressed to a lower dimensional space;(iii) cluster images with similar visual and linguistic information together;(iv) are heavily reliant on test sets with a similar distribution as the training set;(v) repeatedly generate the same captions by matching images and ‘retrieving’ a caption in the joint visual-textual space.Our experiments all point to one fact: that our distributional similarity hypothesis holds.We conclude that, regardless of the image representation, image captioning systems seem to match images and generate captions in a learned joint image-text semantic subspace.", "target": ["Questo articolo presenta un'analisi empirica sul ruolo di diversi tipi di rappresentazioni di immagini e probe e sulle proprietà di queste rappresentazioni per il task di didascalia delle immagini."]} +{"source": "We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory.To account for the fact that typically there are multiple correct SQL queries with the same or very similar semantics, we draw inspiration from syntactic parsing techniques and propose to train our sequence-to-action models with non-deterministic oracles.We evaluate our models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the test set, a 2.1% absolute improvement over the models trained with traditional static oracles assuming a single correct target SQL query.When further combined with the execution-guided decoding strategy, our model sets a new state-of-the-art performance at an execution accuracy of 87.1%.", "target": ["Progettiamo parser incrementali sequence-to-action per task text-to-SQL e otteniamo risultati SOTA. Miglioriamo ulteriormente utilizzando oracoli non deterministici per consentire più sequenze di azioni corrette."]} +{"source": "We propose Efficient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods.In ENAS, a controller learns to discover neural network architectures by searching for an optimal path within a larger model.The controller is trained with policy gradient to select a path that maximizes the expected reward on the validation set.Meanwhile the model corresponding to the selected path is trained to minimize the cross entropy loss.On the Penn Treebank dataset, ENAS can discover a novel architecture thats achieves a test perplexity of 57.8, which is state-of-the-art among automatic model design methods on Penn Treebank.On the CIFAR-10 dataset, ENAS can design novel architectures that achieve a test error of 2.89%, close to the 2.65% achieved by standard NAS (Zoph et al., 2017).Most importantly, our experiments show that ENAS is more than 10x faster and 100x less resource-demanding than NAS.", "target": ["Un approccio che accelera la ricerca dell'architettura neurale di un fattore 10, usando un centesimo delle risorse di calcolo."]} +{"source": "Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech.Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable.In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems.In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data.In a nutshell, the proposed NODE architecture generalizes ensembles of oblivious decision trees, but benefits from both end-to-end gradient-based optimization and the power of multi-layer hierarchical representation learning.With an extensive experimental comparison to the leading GBDT packages on a large number of tabular datasets, we demonstrate the advantage of the proposed NODE architecture, which outperforms the competitors on most of the tasks.We open-source the PyTorch implementation of NODE and believe that it will become a universal framework for machine learning on tabular data.", "target": ["Proponiamo una nuova architettura DNN per il deep learning su dati tabulari"]} +{"source": "Person re-identification (re-ID) aims at identifying the same persons' images across different cameras.However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one.State-of-the-art unsupervised domain adaptation methods for person re-ID transferred the learned knowledge from the source domain by optimizing with pseudo labels created by clustering algorithms on the target domain.Although they achieved state-of-the-art performances, the inevitable label noise caused by the clustering procedure was ignored.Such noisy pseudo labels substantially hinders the model's capability on further improving feature representations on the target domain.In order to mitigate the effects of noisy pseudo labels, we propose to softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner. In addition, the common practice is to adopt both the classification loss and the triplet loss jointly for achieving optimal performances in person re-ID models.However, conventional triplet loss cannot work with softly refined labels.To solve this problem, a novel soft softmax-triplet loss is proposed to support learning with soft pseudo triplet labels for achieving the optimal domain adaptation performance.The proposed MMT framework achieves considerable improvements of 14.4%, 18.2%, 13.1% and 16.4% mAP on Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT unsupervised domain adaptation tasks.", "target": ["Un framework che opera il perfezionamento online delle pseudo label con una nuova loss softmax-triplet per domain adaptation unsupervised su person re-identification."]} +{"source": "We present the first end-to-end verifier of audio classifiers.Compared to existing methods, our approach enables analysis of both, the entire audio processing stage as well as recurrent neural network architectures (e.g., LSTM).The audio processing is verified using novel convex relaxations tailored to feature extraction operations used in audio (e.g., Fast Fourier Transform) while recurrent architectures are certified via a novel binary relaxation for the recurrent unit update.We show the verifier scales to large networks while computing significantly tighter bounds than existing methods for common audio classification benchmarks: on the challenging Google Speech Commands dataset we certify 95% more inputs than the interval approximation (only prior scalable method), for a perturbation of -90dB.", "target": ["Presentiamo il primo approccio per certificare la robustezza delle reti neurali contro le perturbazioni basate sul rumore nel dominio audio."]} +{"source": "Since deep neural networks are over-parameterized, they can memorize noisy examples.We address such memorizing issue in the presence of annotation noise.From the fact that deep neural networks cannot generalize neighborhoods of the features acquired via memorization, we hypothesize that noisy examples do not consistently incur small losses on the network under a certain perturbation.Based on this, we propose a novel training method called Learning with Ensemble Consensus (LEC) that prevents overfitting noisy examples by eliminating them using the consensus of an ensemble of perturbed networks.One of the proposed LECs, LTEC outperforms the current state-of-the-art methods on noisy MNIST, CIFAR-10, and CIFAR-100 in an efficient manner.", "target": ["Questo lavoro presenta un metodo per generare e utilizzare efficacemente gli ensemble per identificare gli esempi rumorosi in presenza di rumore di annotazione."]} +{"source": "Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications.A popular formulation of the problem is an $\\ell_1$ regularized maximum likelihood estimation.Many convex optimization algorithms have been designed to solve this formulation to recover the graph structure.Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.However, it is a challenging task in this case, since the symmetric positive definiteness (SPD) and sparsity of the matrix are not easy to enforce in learned algorithms, and a direct mapping from data to precision matrix may contain many parameters.We propose a deep learning architecture, GLAD, which uses an Alternating Minimization (AM) algorithm as our model inductive bias, and learns the model parameters via supervised learning.We show that GLAD learns a very compact and effective model for recovering sparse graphs from data.", "target": ["Un algoritmo di apprendimento guidato dai dati basato sull'utilizzo dell'ottimizzazione Alternating Minimization per il recupero di grafi sparsi."]} +{"source": "Counterfactual regret minimization (CFR) is a fundamental and effective technique for solving Imperfect Information Games (IIG).However, the original CFR algorithm only works for discrete states and action spaces, and the resulting strategy is maintained as a tabular representation.Such tabular representation limits the method from being directly applied to large games.In this paper, we propose a double neural representation for the IIGs, where one neural network represents the cumulative regret, and the other represents the average strategy. Such neural representations allow us to avoid manual game abstraction and carry out end-to-end optimization.To make the learning efficient, we also developed several novel techniques including a robust sampling method and a mini-batch Monte Carlo Counterfactual Regret Minimization (MCCFR) method, which may be of independent interests. Empirically, on games tractable to tabular approaches, neural strategies trained with our algorithm converge comparably to their tabular counterparts, and significantly outperform those based on deep reinforcement learning. On extremely large games with billions of decision nodes, our approach achieved strong performance while using hundreds of times less memory than the tabular CFR.On head-to-head matches of hands-up no-limit texas hold'em, our neural agent beat the strong agent ABS-CFR by $9.8\\pm4.1$ chips per game.It's a successful application of neural CFR in large games.", "target": ["Abbiamo proposto un doppio framework neurale per risolvere il gioco di informazioni imperfette su larga scala."]} +{"source": "We present the first verification that a neural network for perception tasks producesa correct output within a specified tolerance for every input of interest.We definecorrectness relative to a specification which identifies 1) a state space consisting ofall relevant states of the world and 2) an observation process that produces neuralnetwork inputs from the states of the world.Tiling the state and input spaces witha finite number of tiles, obtaining ground truth bounds from the state tiles andnetwork output bounds from the input tiles, then comparing the ground truth andnetwork output bounds delivers an upper bound on the network output error forany input of interest.Results from two case studies highlight the ability of ourtechnique to deliver tight error bounds for all inputs of interest and show how theerror bounds vary over the state and input spaces.", "target": ["Presentiamo la prima verifica che una rete neurale per task di percezione produce un output corretto entro una tolleranza specificata per ogni input di interesse."]} +{"source": "Deep generative models have achieved remarkable progress in recent years.Despite this progress, quantitative evaluation and comparison of generative models remains as one of the important challenges.One of the most popular metrics for evaluating generative models is the log-likelihood.While the direct computation of log-likelihood can be intractable, it has been recently shown that the log-likelihood of some of the most interesting generative models such as variational autoencoders (VAE) or generative adversarial networks (GAN) can be efficiently estimated using annealed importance sampling (AIS).In this work, we argue that the log-likelihood metric by itself cannot represent all the different performance characteristics of generative models, and propose to use rate distortion curves to evaluate and compare deep generative models.We show that we can approximate the entire rate distortion curve using one single run of AIS for roughly the same computational cost as a single log-likelihood estimate.We evaluate lossy compression rates of different deep generative models such as VAEs, GANs (and its variants) and adversarial autoencoders (AAE) on MNIST and CIFAR10, and arrive at a number of insights not obtainable from log-likelihoods alone.", "target": ["Studiamo approssimazioni del tasso di distorsione per la valutazione di modelli generativi profondi, e mostriamo che le curve del tasso di distorsione forniscono più intuizioni sul modello rispetto alla sola log-likelihood, richiedendo più o meno lo stesso costo computazionale."]} +{"source": "Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time.Given that it is impractical to train separate policies to accommodate all situations the agent may see in the real world, this work proposes to learn how to quickly and effectively adapt online to new tasks.To enable sample-efficient learning, we consider learning online adaptation in the context of model-based reinforcement learning.Our approach uses meta-learning to train a dynamics model prior such that, when combined with recent data, this prior can be rapidly adapted to the local context.Our experiments demonstrate online adaptation for continuous control tasks on both simulated and real-world agents.We first show simulated agents adapting their behavior online to novel terrains, crippled body parts, and highly-dynamic environments.We also illustrate the importance of incorporating online adaptation into autonomous agents that operate in the real world by applying our method to a real dynamic legged millirobot: We demonstrate the agent's learned ability to quickly adapt online to a missing leg, adjust to novel terrains and slopes, account for miscalibration or errors in pose estimation, and compensate for pulling payloads.", "target": ["Un algoritmo meta-RL basato sul modello che permette a un robot reale di adattarsi online in ambienti dinamici"]} +{"source": "Model-free deep reinforcement learning approaches have shown superhuman performance in simulated environments (e.g., Atari games, Go, etc).During training, these approaches often implicitly construct a latent space that contains key information for decision making.In this paper, we learn a forward model on this latent space and apply it to model-based planning in miniature Real-time Strategy game with incomplete information (MiniRTS).We first show that the latent space constructed from existing actor-critic models contains relevant information of the game, and design training procedure to learn forward models.We also show that our learned forward model can predict meaningful future state and is usable for latent space Monte-Carlo Tree Search (MCTS), in terms of win rates against rule-based agents.", "target": ["L'articolo analizza lo spazio latente appreso da approcci model-free in un gioco di informazioni incomplete in miniatura, allena un modello forward nello spazio latente e lo applica alla Monte-Carlo tree search, ottenendo prestazioni positive."]} +{"source": "Several state of the art convolutional networks rely on inter-connecting different layers to ease the flow of information and gradient between their input and output layers.These techniques have enabled practitioners to successfully train deep convolutional networks with hundreds of layers.Particularly, a novel way of interconnecting layers was introduced as the Dense Convolutional Network (DenseNet) and has achieved state of the art performance on relevant image recognition tasks.Despite their notable empirical success, their theoretical understanding is still limited.In this work, we address this problem by analyzing the effect of layer interconnection on the overall expressive power of a convolutional network.In particular, the connections used in DenseNet are compared with other types of inter-layer connectivity.We carry out a tensor analysis on the expressive power inter-connections on convolutional arithmetic circuits (ConvACs) and relate our results to standard convolutional networks.The analysis leads to performance bounds and practical guidelines for design of ConvACs.The generalization of these results are discussed for other kinds of convolutional networks via generalized tensor decompositions.", "target": ["Analizziamo la potenza espressiva delle connessioni usate nelle DenseNets attraverso decomposizioni tensoriali."]} +{"source": "We consider the following central question in the field of Deep Reinforcement Learning (DRL):How can we use implicit human feedback to accelerate and optimize the training of a DRL algorithm?State-of-the-art methods rely on any human feedback to be provided explicitly, requiring the active participation of humans (e.g., expert labeling, demonstrations, etc.).In this work, we investigate an alternative paradigm, where non-expert humans are silently observing (and assessing) the agent interacting with the environment.The human's intrinsic reactions to the agent's behavior is sensed as implicit feedback by placing electrodes on the human scalp and monitoring what are known as event-related electric potentials.The implicit feedback is then used to augment the agent's learning in the RL tasks.We develop a system to obtain and accurately decode the implicit human feedback (specifically error-related event potentials) for state-action pairs in an Atari-type environment.As a baseline contribution, we demonstrate the feasibility of capturing error-potentials of a human observer watching an agent learning to play several different Atari-games using an electroencephalogram (EEG) cap, and then decoding the signals appropriately and using them as an auxiliary reward function to a DRL algorithm with the intent of accelerating its learning of the game.Building atop the baseline, we then make the following novel contributions in our work:(i) We argue that the definition of error-potentials is generalizable across different environments; specifically we show that error-potentials of an observer can be learned for a specific game, and the definition used as-is for another game without requiring re-learning of the error-potentials. (ii) We propose two different frameworks to combine recent advances in DRL into the error-potential based feedback system in a sample-efficient manner, allowing humans to provide implicit feedback while training in the loop, or prior to the training of the RL agent.(iii) Finally, we scale the implicit human feedback (via ErrP) based RL to reasonably complex environments (games) and demonstrate the significance of our approach through synthetic and real user experiments.", "target": ["Usiamo un feedback umano implicito (tramite i potenziali di errore, EEG) per accelerare e ottimizzare il training di un algoritmo DRL, in modo pratico."]} +{"source": "Deep learning has demonstrated abilities to learn complex structures, but they can be restricted by available data.Recently, Consensus Networks (CNs) were proposed to alleviate data sparsity by utilizing features from multiple modalities, but they too have been limited by the size of labeled data.In this paper, we extend CN to Transductive Consensus Networks (TCNs), suitable for semi-supervised learning.In TCNs, different modalities of input are compressed into latent representations, which we encourage to become indistinguishable during iterative adversarial training.To understand TCNs two mechanisms, consensus and classification, we put forward its three variants in ablation studies on these mechanisms.To further investigate TCN models, we treat the latent representations as probability distributions and measure their similarities as the negative relative Jensen-Shannon divergences.We show that a consensus state beneficial for classification desires a stable but imperfect similarity between the representations.Overall, TCNs outperform or align with the best benchmark algorithms given 20 to 200 labeled samples on the Bank Marketing and the DementiaBank datasets.", "target": ["TCN per l'apprendimento multimodale semi-supervised + studio di ablazione dei suoi meccanismi + interpretazioni delle rappresentazioni latenti"]} +{"source": "Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings.However, some of the most popular of these optimization tools - namely Adam, Adagrad and the more recent Amsgrad - remain to be generalized to Riemannian manifolds.We discuss the difficulty of generalizing such adaptive schemes to the most agnostic Riemannian setting, and then provide algorithms and convergence proofs for geodesically convex objectives in the particular case of a product of Riemannian manifolds, in which adaptivity is implemented across manifolds in the cartesian product.Our generalization is tight in the sense that choosing the Euclidean space as Riemannian manifold yields the same algorithms and regret bounds as those that were already known for the standard algorithms.Experimentally, we show faster convergence and to a lower train loss value for Riemannian adaptive methods over their corresponding baselines on the realistic task of embedding the WordNet taxonomy in the Poincare ball.", "target": ["Adattare Adam, Amsgrad, Adagrad ai manifold Riemanniani."]} +{"source": "We study the problem of defending deep neural network approaches for image classification from physically realizable attacks.First, we demonstrate that the two most scalable and effective methods for learning robust models, adversarial training with PGD attacks and randomized smoothing, exhibit very limited effectiveness against three of the highest profile physical attacks.Next, we propose a new abstract adversarial model, rectangular occlusion attacks, in which an adversary places a small adversarially crafted rectangle in an image, and develop two approaches for efficiently computing the resulting adversarial examples.Finally, we demonstrate that adversarial training using our new attack yields image classification models that exhibit high robustness against the physically realizable attacks we study, offering the first effective generic defense against such attacks.", "target": ["Difendersi dagli attacchi fisicamente realizzabili alla classificazione delle immagini"]} +{"source": "Continual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge.However, catastrophic forgetting poses a grand challenge for neural networks performing such learning process.Thus, neural networks that are deployed in the real world often struggle in scenarios where the data distribution is non-stationary (concept drift), imbalanced, or not always fully available, i.e., rare edge cases.We propose a Differentiable Hebbian Consolidation model which is composed of a Differentiable Hebbian Plasticity (DHP) Softmax layer that adds a rapid learning plastic component (compressed episodic memory) to the fixed (slow changing) parameters of the softmax output layer; enabling learned representations to be retained for a longer timescale.We demonstrate the flexibility of our method by integrating well-known task-specific synaptic consolidation methods to penalize changes in the slow weights that are important for each target task.We evaluate our approach on the Permuted MNIST, Split MNIST and Vision Datasets Mixture benchmarks, and introduce an imbalanced variant of Permuted MNIST --- a dataset that combines the challenges of class imbalance and concept drift.Our proposed model requires no additional hyperparameters and outperforms comparable baselines by reducing forgetting.", "target": ["I pesi plastici di Hebbian possono comportarsi come un immagazzinamento di memoria episodica compressa nelle reti neurali e con la combinazione del consolidamento sinaptico specifico del task può migliorare la capacità di alleviare il catastrophic forgetting nel continual learning."]} +{"source": "In order to choose a neural network architecture that will be effective for a particular modeling problem, one must understand the limitations imposed by each of the potential options.These limitations are typically described in terms of information theoretic bounds, or by comparing the relative complexity needed to approximate example functions between different architectures.In this paper, we examine the topological constraints that the architecture of a neural network imposes on the level sets of all the functions that it is able to approximate.This approach is novel for both the nature of the limitations and the fact that they are independent of network depth for a broad family of activation functions.", "target": ["Questo articolo dimostra che le reti neurali skinny non possono approssimare certe funzioni, non importa quanto siano profonde."]} +{"source": "Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs.Inferring loop invariants is one of the main challenges behind automated verification of real-world programs which often contain many loops.In this paper, we present Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants directly from program execution traces.Unlike existing neural networks, CLNs can learn precise and explicit representations of formulas in Satisfiability Modulo Theories (SMT) for loop invariants from program execution traces.We develop a new sound and complete semantic mapping for assigning SMT formulas to continuous truth values that allows CLNs to be trained efficiently.We use CLNs to implement a new inference system for loop invariants, CLN2INV, that significantly outperforms existing approaches on the popular Code2Inv dataset.CLN2INV is the first tool to solve all 124 theoretically solvable problems in the Code2Inv dataset.Moreover, CLN2INV takes only 1.1 second on average for each problem, which is 40 times faster than existing approaches.We further demonstrate that CLN2INV can even learn 12 significantly more complex loop invariants than the ones required for the Code2Inv dataset.", "target": ["Introduciamo la Continuous Logic Network (CLN), una nuova architettura neurale per l'apprendimento automatico di invarianti di loop e formule SMT generali."]} +{"source": "Single cell RNA sequencing (scRNAseq) technology enables quantifying gene expression profiles by individual cells within cancer.Dimension reduction methods have been commonly used for cell clustering analysis and visualization of the data.Current dimension reduction methods tend overly eliminate the expression variations correspond to less dominating characteristics, such we fail to find the homogenious properties of cancer development.In this paper, we proposed a new and clustering analysis method for scRNAseq data, namely BBSC, via implementing a binarization of the gene expression profile into on/off frequency changes with a Boolean matrix factorization.The low rank representation of expression matrix recovered by BBSC increase the resolution in identifying distinct cell types or functions.Application of BBSC on two cancer scRNAseq data successfully discovered both homogeneous and heterogeneous cancer cell clusters.Further finding showed potential in preventing cancer progression.", "target": ["La nostra scoperta fa luce sulla prevenzione della progressione del cancro"]} +{"source": "While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown.In this paper, we show that flows can in fact be extended to discrete events---and under a simple change-of-variables formula not requiring log-determinant-Jacobian computations.Discrete flows have numerous applications.We display proofs of concept under 2 flow architectures: discrete autoregressive flows enable bidirectionality, allowing for example tokens in text to depend on both left-to-right and right-to-left contexts in an exact language model; and discrete bipartite flows (i.e., with layer structure from RealNVP) enable parallel generation such as exact nonautoregressive text modeling.", "target": ["Estendiamo i flussi autoregressivi e RealNVP ai dati discreti."]} +{"source": "We present a Deep Neural Network with Spike Assisted Feature Extraction (SAFE-DNN) to improve robustness of classification under stochastic perturbation of inputs.The proposed network augments a DNN with unsupervised learning of low-level features using spiking neuron network (SNN) with Spike-Time-Dependent-Plasticity (STDP).The complete network learns to ignore local perturbation while performing global feature detection and classification.The experimental results on CIFAR-10 and ImageNet subset demonstrate improved noise robustness for multiple DNN architectures without sacrificing accuracy on clean images.", "target": ["Un'architettura di deep learning robusta al rumore."]} +{"source": "Neural embeddings have been used with great success in Natural Language Processing (NLP) where they provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks.The success of neural embeddings has prompted significant amounts of research into applications in domains other than language.One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling.For both NLP and graph-based tasks, embeddings in high-dimensional Euclidean spaces have been learned.However, recent work has shown that the appropriate isometric space for embedding complex networks is not the flat Euclidean space, but a negatively curved hyperbolic space.We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space.We provide experimental evidence that hyperbolic embeddings significantly outperform Euclidean embeddings on vertex classification tasks for several real-world public datasets.", "target": ["Impariamo embedding neurali di grafi in spazio iperbolico invece che euclideo"]} +{"source": "The International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS) plays a pivotal role in fostering the development of new Knowledge Engineering (KE) tools, and in emphasising the importance of principled approaches for all the different KE aspects that are needed for the successful long-term use of planning in real-world applications. In this paper, as an exercise in synthesis and for the sake of stimulating thoughts and discussion, we review the format of previous ICKEPS, to suggest alternative formats for future competitions, ideally to motivate someone to step up and organise the next ones.", "target": ["Idee per futuri ICKEPS"]} +{"source": "We show that generating English Wikipedia articles can be approached as a multi-document summarization of source documents.We use extractive summarizationto coarsely identify salient information and a neural abstractive model to generatethe article.For the abstractive model, we introduce a decoder-only architecturethat can scalably attend to very long sequences, much longer than typical encoder-decoder architectures used in sequence transduction.We show that this model cangenerate fluent, coherent multi-sentence paragraphs and even whole Wikipediaarticles.When given reference documents, we show it can extract relevant factualinformation as reflected in perplexity, ROUGE scores and human evaluations.", "target": ["Generiamo articoli di Wikipedia abstractively conditioned dal testo del documento di origine."]} +{"source": "Abstract Stochastic gradient descent (SGD) and Adam are commonly used to optimize deep neural networks, but choosing one usually means making tradeoffs between speed, accuracy and stability.Here we present an intuition for why the tradeoffs exist as well as a method for unifying the two in a continuous way.This makes it possible to control the way models are trained in much greater detail.We show that for default parameters, the new algorithm equals or outperforms SGD and Adam across a range of models for image classification tasks and outperforms SGD for language modeling tasks.", "target": ["Un algoritmo per unificare SGD e Adam e uno studio empirico delle sue prestazioni"]} +{"source": "The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging.In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning.In this work, we discover the interaction between sub-tasks from their resulting state-action trajectory sequences using a directed graphical model.We propose a new algorithm based on the generative adversarial imitation learning framework which automatically learns sub-task policies from unsegmented demonstrations.Our approach maximizes the directed information flow in the graphical model between sub-task latent variables and their generated trajectories.We also show how our approach connects with the existing Options framework, which is commonly used to learn hierarchical policies.", "target": ["Apprendere policy gerarchiche da dimostrazioni non segmentate usando informazioni dirette"]} +{"source": "The Convolutional Neural Network (CNN) has been successfully applied in many fields during recent decades; however it lacks the ability to utilize prior domain knowledge when dealing with many realistic problems.We present a framework called Geometric Operator Convolutional Neural Network (GO-CNN) that uses domain knowledge, wherein the kernel of the first convolutional layer is replaced with a kernel generated by a geometric operator function.This framework integrates many conventional geometric operators, which allows it to adapt to a diverse range of problems.Under certain conditions, we theoretically analyze the convergence and the bound of the generalization errors between GO-CNNs and common CNNs.Although the geometric operator convolution kernels have fewer trainable parameters than common convolution kernels, the experimental results indicate that GO-CNN performs more accurately than common CNN on CIFAR-10/100.Furthermore, GO-CNN reduces dependence on the amount of training examples and enhances adversarial stability.", "target": ["I tradizionali algoritmi di elaborazione delle immagini sono combinati con le reti neurali convoluzionali, una nuova rete neurale."]} +{"source": "Determinantal point processes (DPPs) is an effective tool to deliver diversity on multiple machine learning and computer vision tasks.Under deep learning framework, DPP is typically optimized via approximation, which is not straightforward and has some conflict with diversity requirement.We note, however, there has been no deep learning paradigms to optimize DPP directly since it involves matrix inversion which may result in highly computational instability.This fact greatly hinders the wide use of DPP on some specific objectives where DPP serves as a term to measure the feature diversity.In this paper, we devise a simple but effective algorithm to address this issue to optimize DPP term directly expressed with L-ensemble in spectral domain over gram matrix, which is more flexible than learning on parametric kernels.By further taking into account some geometric constraints, our algorithm seeks to generate valid sub-gradients of DPP term in case when the DPP gram matrix is not invertible (no gradients exist in this case).In this sense, our algorithm can be easily incorporated with multiple deep learning tasks.Experiments show the effectiveness of our algorithm, indicating promising performance for practical learning problems.", "target": ["Abbiamo proposto un metodo specifico di back-propagation attraverso un appropriato sub-gradiente spettrale per integrare il determinantal point process nel framework del deep learning."]} +{"source": "The quality of a machine translation system depends largely on the availability of sizable parallel corpora.For the recently popular Neural Machine Translation (NMT) framework, data sparsity problem can become even more severe.With large amount of tunable parameters, the NMT model may overfit to the existing language pairs while failing to understand the general diversity in language.In this paper, we advocate to broadcast every sentence pair as two groups of similar sentences to incorporate more diversity in language expressions, which we name as parallel cluster.Then we define a more general cluster-to-cluster correspondence score and train our model to maximize this score.Since direct maximization is difficult, we derive its lower-bound as our surrogate objective, which is found to generalize point-point Maximum Likelihood Estimation (MLE) and point-to-cluster Reward Augmented Maximum Likelihood (RAML) algorithms as special cases.Based on this novel objective function, we delineate four potential systems to realize our cluster-to-cluster framework and test their performances in three recognized translation tasks, each task with forward and reverse translation directions.In each of the six experiments, our proposed four parallel systems have consistently proved to outperform the MLE baseline, RL (Reinforcement Learning) and RAML systems significantly.Finally, we have performed case study to empirically analyze the strength of the cluster-to-cluster NMT framework.", "target": ["Inventiamo un nuovo framework cluster-to-cluster per il training NMT, che può comprendere meglio la diversità tra la lingua di origine e quella di destinazione."]} +{"source": "The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems.In this work, we study the learning to explain the problem in the scope of inductive logic programming (ILP).We propose Neural Logic Inductive Learning (NLIL), an efficient differentiable ILP framework that learns first-order logic rules that can explain the patterns in the data.In experiments, compared with the state-of-the-art models, we find NLIL is able to search for rules that are x10 times longer while remaining x3 times faster.We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities.", "target": ["Un efficiente modello ILP differenziabile che impara regole logiche del primo ordine che possono spiegare i dati."]} +{"source": "Neural network-based classifiers parallel or exceed human-level accuracy on many common tasks and are used in practical systems.Yet, neural networks are susceptible to adversarial examples, carefully perturbed inputs that cause networks to misbehave in arbitrarily chosen ways.When generated with standard methods, these examples do not consistently fool a classifier in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations.Adversarial examples generated using standard techniques require complete control over direct input to the classifier, which is impossible in many real-world systems.We introduce the first method for constructing real-world 3D objects that consistently fool a neural network across a wide distribution of angles and viewpoints.We present a general-purpose algorithm for generating adversarial examples that are robust across any chosen distribution of transformations.We demonstrate its application in two dimensions, producing adversarial images that are robust to noise, distortion, and affine transformation.Finally, we apply the algorithm to produce arbitrary physical 3D-printed adversarial objects, demonstrating that our approach works end-to-end in the real world.Our results show that adversarial examples are a practical concern for real-world systems.", "target": ["Introduciamo un nuovo metodo per sintetizzare adversarial example robusti nel mondo fisico e lo usiamo per fabbricare i primi oggetti 3D di avversione."]} +{"source": "Representations of sets are challenging to learn because operations on sets should be permutation-invariant.To this end, we propose a Permutation-Optimisation module that learns how to permute a set end-to-end.The permuted set can be further processed to learn a permutation-invariant representation of that set, avoiding a bottleneck in traditional set models.We demonstrate our model's ability to learn permutations and set representations with either explicit or implicit supervision on four datasets, on which we achieve state-of-the-art results: number sorting, image mosaics, classification from image mosaics, and visual question answering.", "target": ["Impara come permutare un insieme, poi codifica l'insieme permutato con RNN per ottenere una rappresentazione dell'insieme."]} +{"source": "The physical design of a robot and the policy that controls its motion are inherently coupled.However, existing approaches largely ignore this coupling, instead choosing to alternate between separate design and control phases, which requires expert intuition throughout and risks convergence to suboptimal designs.In this work, we propose a method that jointly optimizes over the physical design of a robot and the corresponding control policy in a model-free fashion, without any need for expert supervision.Given an arbitrary robot morphology, our method maintains a distribution over the design parameters and uses reinforcement learning to train a neural network controller.Throughout training, we refine the robot distribution to maximize the expected reward.This results in an assignment to the robot parameters and neural network policy that are jointly optimal.We evaluate our approach in the context of legged locomotion, and demonstrate that it discovers novel robot designs and walking gaits for several different morphologies, achieving performance comparable to or better than that of hand-crafted designs.", "target": ["Utilizzare il deep reinforcement learning per progettare gli attributi fisici di un robot insieme ad una policy di controllo."]} +{"source": "Deep learning enables training of large and flexible function approximators from scratch at the cost of large amounts of data.Applications of neural networks often consider learning in the context of a single task.However, in many scenarios what we hope to learn is not just a single task, but a model that can be used to solve multiple different tasks.Such multi-task learning settings have the potential to improve data efficiency and generalization by sharing data and representations across tasks.However, in some challenging multi-task learning settings, particularly in reinforcement learning, it is very difficult to learn a single model that can solve all the tasks while realizing data efficiency and performance benefits.Learning each of the tasks independently from scratch can actually perform better in such settings, but it does not benefit from the representation sharing that multi-task learning can potentially provide.In this work, we develop an approach that endows a single model with the ability to represent both extremes: joint training and independent training.To this end, we introduce matrix-interleaving (Mint), a modification to standard neural network models that projects the activations for each task into a different learned subspace, represented by a per-task and per-layer matrix.By learning these matrices jointly with the other model parameters, the optimizer itself can decide how much to share representations between tasks.On three challenging multi-task supervised learning and reinforcement learning problems with varying degrees of shared task structure, we find that this model consistently matches or outperforms joint training and independent training, combining the best elements of both.", "target": ["Proponiamo un approccio che conferisce a un singolo modello la capacità di rappresentare entrambi gli estremi: joint training e training indipendente, il che porta a un efficace apprendimento multi-task."]} +{"source": "Training agents to operate in one environment often yields overfitted models that are unable to generalize to the changes in that environment.However, due to the numerous variations that can occur in the real-world, the agent is often required to be robust in order to be useful.This has not been the case for agents trained with reinforcement learning (RL) algorithms.In this paper, we investigate the overfitting of RL agents to the training environments in visual navigation tasks.Our experiments show that deep RL agents can overfit even when trained on multiple environments simultaneously. We propose a regularization method which combines RL with supervised learning methods by adding a term to the RL objective that would encourage the invariance of a policy to variations in the observations that ought not to affect the action taken.The results of this method, called invariance regularization, show an improvement in the generalization of policies to environments not seen during training.", "target": ["Proponiamo un termine di regolarizzazione che, quando aggiunto all'obiettivo di reinforcement learning, permette alla policy di massimizzare la reward e contemporaneamente imparare ad essere invariante ai cambiamenti irrilevanti all'interno dell'input."]} +{"source": "Though visual information has been introduced for enhancing neural machine translation (NMT), its effectiveness strongly relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations.In this paper, we present a universal visual representation learned over the monolingual corpora with image annotations, which overcomes the lack of large-scale bilingual sentence-image pairs, thereby extending image applicability in NMT.In detail, a group of images with similar topics to the source sentence will be retrieved from a light topic-image lookup table learned over the existing sentence-image pairs, and then is encoded as image representations by a pre-trained ResNet.An attention layer with a gated weighting is to fuse the visual information and text information as input to the decoder for predicting target translations.In particular, the proposed method enables the visual information to be integrated into large-scale text-only NMT in addition to the multimodel NMT.Experiments on four widely used translation datasets, including the WMT'16 English-to-Romanian, WMT'14 English-to-German, WMT'14 English-to-French, and Multi30K, show that the proposed approach achieves significant improvements over strong baselines.", "target": ["Questo lavoro ha proposto una rappresentazione visiva universale per la traduzione automatica neurale (NMT) utilizzando immagini recuperate con argomenti simili alla frase di origine, estendendo l'applicabilità delle immagini nella NMT."]} +{"source": "This paper introduces a novel framework for learning algorithms to solve online combinatorial optimization problems.Towards this goal, we introduce a number of key ideas from traditional algorithms and complexity theory.First, we draw a new connection between primal-dual methods and reinforcement learning.Next, we introduce the concept of adversarial distributions (universal and high-entropy training sets), which are distributions that encourage the learner to find algorithms that work well in the worst case.We test our new ideas on a number of optimization problem such as the AdWords problem, the online knapsack problem, and the secretary problem.Our results indicate that the models have learned behaviours that are consistent with the traditional optimal algorithms for these problems.", "target": ["Combinando idee dalla progettazione di algoritmi tradizionali e dal reinforcement learning, introduciamo un nuovo framework per l'apprendimento di algoritmi che risolvono problemi di ottimizzazione combinatoria online."]} +{"source": "Despite their popularity and successes, deep neural networks are poorly understood theoretically and treated as 'black box' systems.Using a functional view of these networks gives us a useful new lens with which to understand them.This allows us us to theoretically or experimentally probe properties of these networks, including the effect of standard initializations, the value of depth, the underlying loss surface, and the origins of generalization.One key result is that generalization results from smoothness of the functional approximation, combined with a flat initial approximation.This smoothness increases with number of units, explaining why massively overparamaterized networks continue to generalize well.", "target": ["Un approccio funzionale rivela che l'inizializzazione flat, preservata dalla gradient descent, porta alla capacità di generalizzazione."]} +{"source": "It is well-known that deeper neural networks are harder to train than shallower ones.In this short paper, we use the (full) eigenvalue spectrum of the Hessian to explore how the loss landscape changes as the network gets deeper, and as residual connections are added to the architecture.Computing a series of quantitative measures on the Hessian spectrum, we show that the Hessian eigenvalue distribution in deeper networks has substantially heavier tails (equivalently, more outlier eigenvalues), which makes the network harder to optimize with first-order methods.We show that adding residual connections mitigates this effect substantially, suggesting a mechanism by which residual connections improve training.", "target": ["La profondità della rete aumenta gli autovalori outlier nell'Hessiana. Le residual connection mitigano questo fenomeno."]} +{"source": "In the context of optimization, a gradient of a neural network indicates the amount a specific weight should change with respect to the loss.Therefore, small gradients indicate a good value of the weight that requires no change and can be kept frozen during training.This paper provides an experimental study on the importance of a neural network weights, and to which extent do they need to be updated.We wish to show that starting from the third epoch, freezing weights which have no informative gradient and are less likely to be changed during training, results in a very slight drop in the overall accuracy (and in sometimes better).We experiment on the MNIST, CIFAR10 and Flickr8k datasets using several architectures (VGG19,ResNet-110 and DenseNet-121).On CIFAR10, we show that freezing 80% of the VGG19 network parameters from the third epoch onwards results in 0.24% drop in accuracy, while freezing 50% of Resnet-110 parameters results in 0.9% drop in accuracy and finally freezing 70% of Densnet-121 parameters results in 0.57% drop in accuracy.Furthermore, to experiemnt with real-life applications, we train an image captioning model with attention mechanism on the Flickr8k dataset using LSTM networks, freezing 60% of the parameters from the third epoch onwards, resulting in a better BLEU-4 score than the fully trained model.Our source code can be found in the appendix.", "target": ["Un paper sperimentale che dimostra la quantità di pesi ridondanti che possono essere congelati solo a partire dalla terza epoch, con solo un leggerissimo calo di precisione."]} +{"source": "Like humans, deep networks learn better when samples are organized and introduced in a meaningful order or curriculum.While conventional approaches to curriculum learning emphasize the difficulty of samples as the core incremental strategy, it forces networks to learn from small subsets of data while introducing pre-computation overheads.In this work, we propose Learning with Incremental Labels and Adaptive Compensation (LILAC), which introduces a novel approach to curriculum learning.LILAC emphasizes incrementally learning labels instead of incrementally learning difficult samples.It works in two distinct phases: first, in the incremental label introduction phase, we unmask ground-truth labels in fixed increments during training, to improve the starting point from which networks learn.In the adaptive compensation phase, we compensate for failed predictions by adaptively altering the target vector to a smoother distribution.We evaluate LILAC against the closest comparable methods in batch and curriculum learning and label smoothing, across three standard image benchmarks, CIFAR-10, CIFAR-100, and STL-10.We show that our method outperforms batch learning with higher mean recognition accuracy as well as lower standard deviation in performance consistently across all benchmarks.We further extend LILAC to state-of-the-art performance across CIFAR-10 using simple data augmentation while exhibiting label order invariance among other important properties.", "target": ["Un nuovo approccio al curriculum learning attraverso l'apprendimento incrementale delle label e lo smoothing adattivo delle label per i sample mal classificati che aumenta la performance media e diminuisce la deviazione standard."]} +{"source": "Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare.Learning representations for words in the ``long tail'' of this distribution requires enormous amounts of data. Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-train embeddings on external data, or treat all rare words as out-of-vocabulary words with a unique representation.We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task.We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.", "target": ["Proponiamo un metodo per trattare le parole rare calcolando la loro embedding dalle definizioni."]} +{"source": "The capability of reliably detecting out-of-distribution samples is one of the key factors in deploying a good classifier, as the test distribution always does not match with the training distribution in most real-world applications.In this work, we propose a deep generative classifier which is effective to detect out-of-distribution samples as well as classify in-distribution samples, by integrating the concept of Gaussian discriminant analysis into deep neural networks.Unlike the discriminative (or softmax) classifier that only focuses on the decision boundary partitioning its latent space into multiple regions, our generative classifier aims to explicitly model class-conditional distributions as separable Gaussian distributions.Thereby, we can define the confidence score by the distance between a test sample and the center of each distribution.Our empirical evaluation on multi-class images and tabular data demonstrate that the generative classifier achieves the best performances in distinguishing out-of-distribution samples, and also it can be generalized well for various types of deep neural networks.", "target": ["Questo articolo propone un deep generative classifier che è efficace per rilevare i sample out-of-distribution così come per classificare i sample in-distribution, integrando il concetto di analisi discriminante gaussiana nelle deep neural network."]} +{"source": "One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts.However, these linguistic features are impacted in a similar but different fashion by the normal aging process.Aging is therefore a confounding factor, whose effects have been hard for machine learning classifiers to isolate. In this paper, we show that deep neural network (DNN) classifiers can infer ages from linguistic features, which is an entanglement that could lead to unfairness across age groups.We show this problem is caused by undesired activations of v-structures in causality diagrams, and it could be addressed with fair representation learning.We build neural network classifiers that learn low-dimensional representations reflecting the impacts of dementia yet discarding the effects of age.To evaluate these classifiers, we specify a model-agnostic score $\\Delta_{eo}^{(N)}$ measuring how classifier results are disentangled from age.Our best models outperform baseline neural network classifiers in disentanglement, while compromising accuracy by as little as 2.56\\% and 2.25\\% on DementiaBank and the Famous People dataset respectively.", "target": ["Mostrare che l'età confonde l'individuazione del deterioramento cognitivo + risolvere con representation learning fair + proporre metriche e modelli."]} +{"source": "Much of the focus in the design of deep neural networks had been on improving accuracy, leading to more powerful yet highly complex network architectures that are difficult to deploy in practical scenarios. As a result, there has been a recent interest in the design of quantitative metrics for evaluating deep neural networks that accounts for more than just model accuracy as the sole indicator of network performance. In this study, we continue the conversation towards universal metrics for evaluating the performance of deep neural networks for practical on-device edge usage by introducing NetScore, a new metric designed specifically to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network. In what is one of the largest comparative analysis between deep neural networks in literature, the NetScore metric, the top-1 accuracy metric, and the popular information density metric were compared across a diverse set of 60 different deep convolutional neural networks for image classification on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) dataset. The evaluation results across these three metrics for this diverse set of networks are presented in this study to act as a reference guide for practitioners in the field.", "target": ["Introduciamo NetScore, una nuova metrica progettata per fornire una valutazione quantitativa dell'equilibrio tra precisione, complessità computazionale e complessità dell'architettura di una deep neural network."]} +{"source": "Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, especially white-box targeted attacks.This paper studies the problem of how aggressive white-box targeted attacks can be to go beyond widely used Top-1 attacks.We propose to learn ordered Top-k attacks (k>=1), which enforce the Top-k predicted labels of an adversarial example to be the k (randomly) selected and ordered labels (the ground-truth label is exclusive).Two methods are presented.First, we extend the vanilla Carlini-Wagner (C&W) method and use it as a strong baseline.Second, we present an adversarial distillation framework consisting of two components:(i) Computing an adversarial probability distribution for any given ordered Top-$k$ targeted labels.(ii) Learning adversarial examples by minimizing the Kullback-Leibler (KL) divergence between the adversarial distribution and the predicted distribution, together with the perturbation energy penalty.In computing adversarial distributions, we explore how to leverage label semantic similarities, leading to knowledge-oriented attacks.In experiments, we test Top-k (k=1,2,5,10) attacks in the ImageNet-1000 val dataset using two popular DNNs trained with the clean ImageNet-1000 train dataset, ResNet-50 and DenseNet-121.Overall, the adversarial distillation approach obtains the best results, especially by large margin when computation budget is limited..It reduces the perturbation energy consistently with the same attack success rate on all the four k's, and improve the attack success rate by large margin against the modified C&W method for k=10.", "target": ["Attacchi top-k adversarial ordinati"]} +{"source": "Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success.However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend.In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank.We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP.Our model's training time is on par or faster and its number of parameters on par or lower than previous models.It leverages a large, adjustable neighborhood for classification and can be easily combined with any neural network.We show that this model outperforms several recently proposed methods for semi-supervised classification in the most thorough study done so far for GCN-like models.Our implementation is available online.", "target": ["La propagazione personalizzata delle previsioni neurali (PPNP) migliora le graph neural network separandole in predizione e propagazione tramite PageRank personalizzato."]} +{"source": "Most of recent work in cross-lingual word embeddings is severely Anglocentric.The vast majority of lexicon induction evaluation dictionaries are between English and another language, and the English embedding space is selected by default as the hub when learning in a multilingual setting.With this work, however, we challenge these practices.First, we show that the choice of hub language can significantly impact downstream lexicon induction performance.Second, we both expand the current evaluation dictionary collection to include all language pairs using triangulation, and also create new dictionaries for under-represented languages.Evaluating established methods over all these language pairs sheds light into their suitability and presents new challenges for the field.Finally, in our analysis we identify general guidelines for strong cross-lingual embeddings baselines, based on more than just Anglocentric experiments.", "target": ["La scelta della lingua principale (di destinazione) influisce sulla qualità degli embedding multilinguali, che non dovrebbero essere valutate solo su dizionari anglo-centrici."]} +{"source": "Interpreting generative adversarial network (GAN) training as approximate divergence minimization has been theoretically insightful, has spurred discussion, and has lead to theoretically and practically interesting extensions such as f-GANs and Wasserstein GANs.For both classic GANs and f-GANs, there is an original variant of training and a \"non-saturating\" variant which uses an alternative form of generator update.The original variant is theoretically easier to study, but the alternative variant frequently performs better and is recommended for use in practice.The alternative generator update is often regarded as a simple modification to deal with optimization issues, and it appears to be a common misconception that the two variants minimize the same divergence.In this short note we derive the divergences approximately minimized by the original and alternative variants of GAN and f-GAN training.This highlights important differences between the two variants.For example, we show that the alternative variant of KL-GAN training actually minimizes the reverse KL divergence, and that the alternative variant of conventional GAN training minimizes a \"softened\" version of the reverse KL.We hope these results may help to clarify some of the theoretical discussion surrounding the divergence minimization view of GAN training.", "target": ["Il tipico training GAN non ottimizza Jensen-Shannon, ma qualcosa simile ad una divergenza KL inversa."]} +{"source": "REINFORCE can be used to train models in structured prediction settings to directly optimize the test-time objective.However, the common case of sampling one prediction per datapoint (input) is data-inefficient.We show that by drawing multiple samples (predictions) per datapoint, we can learn with significantly less data, as we freely obtain a REINFORCE baseline to reduce variance.Additionally we derive a REINFORCE estimator with baseline, based on sampling without replacement.Combined with a recent technique to sample sequences without replacement using Stochastic Beam Search, this improves the training procedure for a sequence model that predicts the solution to the Travelling Salesman Problem.", "target": ["Mostriamo che estraendo più sample (predizioni) per ogni input (datapoint), possiamo imparare con meno dati poiché otteniamo liberamente una baseline REINFORCE."]} +{"source": "Reinforcement learning (RL) is a powerful technique to train an agent to perform a task. However, an agent that is trained using RL is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations. Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing in its environment. We use a generator network to propose tasks for the agent to try to achieve, each task being specified as reaching a certain parametrized subset of the state-space. The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent. Our method thus automatically produces a curriculum of tasks for the agent to learn. We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment (Videos and code available at: https://sites.google.com/view/goalgeneration4rl).Our method can also learn to achieve tasks with sparse rewards, which pose significant challenges for traditional RL methods.", "target": ["Risolviamo in modo efficiente i problemi multi-task con un algoritmo di generazione automatica del curriculum basato su un modello generativo che tiene traccia delle prestazioni dell'agente di apprendimento."]} +{"source": "A wide range of defenses have been proposed to harden neural networks against adversarial attacks.However, a pattern has emerged in which the majority of adversarial defenses are quickly broken by new attacks. Given the lack of success at generating robust defenses, we are led to ask a fundamental question: Are adversarial attacks inevitable?This paper analyzes adversarial examples from a theoretical perspective, and identifies fundamental bounds on the susceptibility of a classifier to adversarial attacks. We show that, for certain classes of problems, adversarial examples are inescapable. Using experiments, we explore the implications of theoretical guarantees for real-world problems and discuss how factors such as dimensionality and image complexity limit a classifier's robustness against adversarial examples.", "target": ["Questo articolo identifica classi di problemi per i quali gli adversarial example sono inevitabili, e deriva limiti fondamentali sulla suscettibilità di qualsiasi classificatore agli adversarial example."]} +{"source": "For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks.Activation maps, however, occupy a large memory footprint during both the training and inference step when using mini-batches of inputs.One way to reduce this large memory footprint is to reduce the precision of activations.However, past works have shown that reducing the precision of activations hurts model accuracy.We study schemes to train networks from scratch using reduced-precision activations without hurting accuracy.We reduce the precision of activation maps (along with model parameters) and increase the number of filter maps in a layer, and find that this scheme matches or surpasses the accuracy of the baseline full-precision network.As a result, one can significantly improve the execution efficiency (e.g. reduce dynamic memory footprint, memory band- width and computational energy) and speed up the training and inference process with appropriate hardware support.We call our scheme WRPN -- wide reduced-precision networks.We report results and show that WRPN scheme is better than previously reported accuracies on ILSVRC-12 dataset while being computationally less expensive compared to previously reported reduced-precision networks.", "target": ["Abbassando la precisione (a 4-bit, 2-bit e persino binario) e allargando i banchi di filtri si ottiene una rete accurata come quelle ottenute con pesi e attivazioni FP32."]} +{"source": "We investigate methods for semi-supervised learning (SSL) of a neural linear-chain conditional random field (CRF) for Named Entity Recognition (NER) by treating the tagger as the amortized variational posterior in a generative model of text given tags.We first illustrate how to incorporate a CRF in a VAE, enabling end-to-end training on semi-supervised data.We then investigate a series of increasingly complex deep generative models of tokens given tags enabled by end-to-end optimization, comparing the proposed models against supervised and strong CRF SSL baselines on the Ontonotes5 NER dataset.We find that our best proposed model consistently improves performance by $\\approx 1\\%$ F1 in low- and moderate-resource regimes and easily addresses degenerate model behavior in a more difficult, partially supervised setting.", "target": ["Incorporiamo una CRF in una VAE di token e tag NER per l'apprendimento semi-supervised e mostriamo miglioramenti in setting low-resource."]} +{"source": "To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights.One common technique for quantizing neural networks is the straight-through gradient method, which enables back-propagation through the quantization mapping.Despite its empirical success, little is understood about why the straight-through gradient method works.Building upon a novel observation that the straight-through gradient method is in fact identical to the well-known Nesterov’s dual-averaging algorithm on a quantization constrained optimization problem, we propose a more principled alternative approach, called ProxQuant , that formulates quantized network training as a regularized learning problem instead and optimizes it via the prox-gradient method.ProxQuant does back-propagation on the underlying full-precision vector and applies an efficient prox-operator in between stochastic gradient steps to encourage quantizedness.For quantizing ResNets and LSTMs, ProxQuant outperforms state-of-the-art results on binary quantization and is on par with state-of-the-art on multi-bit quantization.For binary quantization, our analysis shows both theoretically and experimentally that ProxQuant is more stable than the straight-through gradient method (i.e. BinaryConnect), challenging the indispensability of the straight-through gradient method and providing a powerful alternative.", "target": ["Un framework teoricamente valido per la quantizzazione del modello usando il metodo del gradiente prossimale."]} +{"source": "The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains.Devising a definitive defense against such attacks is proven to be challenging, and the methods relying on detecting adversarial samples are only valid when the attacker is oblivious to the detection mechanism.In this paper, we consider the adversarial detection problem under the robust optimization framework.We partition the input space into subspaces and train adversarial robust subspace detectors using asymmetrical adversarial training (AAT).The integration of the classifier and detectors presents a detection mechanism that provides a performance guarantee to the adversary it considered.We demonstrate that AAT promotes the learning of class-conditional distributions, which further gives rise to generative detection/classification approaches that are both robust and more interpretable.We provide comprehensive evaluations of the above methods, and demonstrate their competitive performances and compelling properties on adversarial detection and robust classification problems.", "target": ["Una nuova tecnica di modellazione generativa basata sull'adversarial training asimmetrico, e le sue applicazioni al rilevamento di adversarial example e alla classificazione robusta"]} +{"source": "Exploration is a key component of successful reinforcement learning, but optimal approaches are computationally intractable, so researchers have focused on hand-designing mechanisms based on exploration bonuses and intrinsic reward, some inspired by curious behavior in natural systems. In this work, we propose a strategy for encoding curiosity algorithms as programs in a domain-specific language and searching, during a meta-learning phase, for algorithms that enable RL agents to perform well in new domains. Our rich language of programs, which can combine neural networks with other building blocks including nearest-neighbor modules and can choose its own loss functions, enables the expression of highly generalizable programs that perform well in domains as disparate as grid navigation with image input, acrobot, lunar lander, ant and hopper. To make this approach feasible, we develop several pruning techniques, including learning to predict a program's success based on its syntactic properties. We demonstrate the effectiveness of the approach empirically, finding curiosity strategies that are similar to those in published literature, as well as novel strategies that are competitive with them and generalize well.", "target": ["Il meta-learning con algoritmi di curiosity attraverso la ricerca in un ricco spazio di programmi produce nuovi meccanismi che generalizzano attraverso domini di reinforcement learning molto diversi."]} +{"source": "Many machine learning algorithms represent input data with vector embeddings or discrete codes.When inputs exhibit compositional structure (e.g. objects built from parts or procedures from subroutines), it is natural to ask whether this compositional structure is reflected in the the inputs’ learned representations.While the assessment of compositionality in languages has received significant attention in linguistics and adjacent fields, the machine learning literature lacks general-purpose tools for producing graded measurements of compositional structure in more general (e.g. vector-valued) representation spaces.We describe a procedure for evaluating compositionality by measuring how well the true representation-producing model can be approximated by a model that explicitly composes a collection of inferred representational primitives.We use the procedure to provide formal and empirical characterizations of compositional structure in a variety of settings, exploring the relationship between compositionality and learning dynamics, human judgments, representational similarity, and generalization.", "target": ["Questo articolo propone una semplice procedura per valutare la struttura compositiva nelle rappresentazioni apprese, e usa la procedura per esplorare il ruolo della compositività in quattro problemi di apprendimento."]} +{"source": "In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem.The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled constrained programming algorithm as a building block in the architecture to enforce constraints.With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (29.7% improvement in some cases in F1 scores and even larger improvement for pseudoknotted structures) and runs as efficient as the fastest algorithms in terms of inference time.", "target": ["Un modello DL per la predizione della struttura secondaria dell'RNA, che usa un algoritmo unrolled nell'architettura per far rispettare i vincoli."]} +{"source": "Learning in recurrent neural networks (RNNs) is most often implemented by gradient descent using backpropagation through time (BPTT), but BPTT does not model accurately how the brain learns.Instead, many experimental results on synaptic plasticity can be summarized as three-factor learning rules involving eligibility traces of the local neural activity and a third factor.We present here eligibility propagation (e-prop), a new factorization of the loss gradients in RNNs that fits the framework of three factor learning rules when derived for biophysical spiking neuron models.When tested on the TIMIT speech recognition benchmark, it is competitive with BPTT both for training artificial LSTM networks and spiking RNNs.Further analysis suggests that the diversity of learning signals and the consideration of slow internal neural dynamics are decisive to the learning efficiency of e-prop.", "target": ["Presentiamo la eligibility propagation un'alternativa al BPTT che è compatibile con i dati sperimentali sulla synaptic plasticity e compete con il BPTT sui benchmark di apprendimento automatico."]} +{"source": "Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems.RNN policies, however, are particularly difficult to explain, understand, and analyze due to their use of continuous-valued memory vectors and observation features.In this paper, we introduce a new technique, Quantized Bottleneck Insertion, to learn finite representations of these vectors and features.The result is a quantized representation of the RNN that can be analyzed to improve our understanding of memory use and general behavior.We present results of this approach on synthetic environments and six Atari games.The resulting finite representations are surprisingly small in some cases, using as few as 3 discrete memory states and 10 observations for a perfect Pong policy.We also show that these finite policy representations lead to improved interpretability.", "target": ["Estrazione di un automa a stati finiti da una rete neurale ricorrente tramite quantizzazione ai fini dell'interpretabilità con esperimenti su Atari."]} +{"source": "Building upon the recent success of deep reinforcement learning methods, we investigate the possibility of on-policy reinforcement learning improvement by reusing the data from several consecutive policies.On-policy methods bring many benefits, such as ability to evaluate each resulting policy.However, they usually discard all the information about the policies which existed before.In this work, we propose adaptation of the replay buffer concept, borrowed from the off-policy learning setting, to the on-policy algorithms.To achieve this, the proposed algorithm generalises the Q-, value and advantage functions for data from multiple policies.The method uses trust region optimisation, while avoiding some of the common problems of the algorithms such as TRPO or ACKTR: it uses hyperparameters to replace the trust region selection heuristics, as well as the trainable covariance matrix instead of the fixed one.In many cases, the method not only improves the results comparing to the state-of-the-art trust region on-policy learning algorithms such as ACKTR and TRPO, but also with respect to their off-policy counterpart DDPG.", "target": ["Studiamo l'evidenza teorica e pratica del miglioramento del reinforcement learning on-policy riutilizzando i dati di diverse policy consecutive."]} +{"source": "Convolutional Neural Networks (CNN) have been successful in processing data signals that are uniformly sampled in the spatial domain (e.g., images).However, most data signals do not natively exist on a grid, and in the process of being sampled onto a uniform physical grid suffer significant aliasing error and information loss.Moreover, signals can exist in different topological structures as, for example, points, lines, surfaces and volumes.It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).To this end, we develop mathematical formulations for Non-Uniform Fourier Transforms (NUFT) to directly, and optimally, sample nonuniform data signals of different topologies defined on a simplex mesh into the spectral domain with no spatial sampling error.The spectral transform is performed in the Euclidean space, which removes the translation ambiguity from works on the graph spectrum.Our representation has four distinct advantages: (1) the process causes no spatial sampling error during initial sampling, (2) the generality of this approach provides a unified framework for using CNNs to analyze signals of mixed topologies, (3) it allows us to leverage state-of-the-art backbone CNN architectures for effective learning without having to design a particular architecture for a particular data structure in an ad-hoc fashion, and (4) the representation allows weighted meshes where each element has a different weight (i.e., texture) indicating local properties.We achieve good results on-par with state-of-the-art for 3D shape retrieval task, and new state-of-the-art for point cloud to surface reconstruction task.", "target": ["Usiamo la trasformazione non euclidea di Fourier delle forme definite da un complesso simpliciale per il deep learning, ottenendo risultati significativamente migliori rispetto alle tecniche di point-based sampling utilizzate nell'attuale letteratura sull'apprendimento 3D."]} +{"source": "Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences.Traditional score-based casual discovery methods rely on various local heuristics to search for a Directed Acyclic Graph (DAG) according to a predefined score function.While these methods, e.g., greedy equivalence search, may have attractive results with infinite samples and certain model assumptions, they are less satisfactory in practice due to finite data and possible violation of assumptions.Motivated by recent advances in neural combinatorial optimization, we propose to use Reinforcement Learning (RL) to search for the DAG with the best scoring.Our encoder-decoder model takes observable data as input and generates graph adjacency matrices that are used to compute rewards.The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity.In contrast with typical RL applications where the goal is to learn a policy, we use RL as a search strategy and our final output would be the graph, among all graphs generated during training, that achieves the best reward.We conduct experiments on both synthetic and real datasets, and show that the proposed approach not only has an improved search ability but also allows for a flexible score function under the acyclicity constraint.", "target": ["Applichiamo il reinforcement learning alla scoperta causale basata sullo score e otteniamo risultati promettenti su dataset sia sintetici sia reali"]} +{"source": "The topic modeling discovers the latent topic probability of given the text documents.To generate the more meaningful topic that better represents the given document, we proposed a universal method which can be used in the data preprocessing stage.The method consists of three steps.First, it generates the word/word-pair from every single document.Second, it applies a two way parallel TF-IDF algorithm to word/word-pair for semantic filtering.Third, it uses the k-means algorithm to merge the word pairs that have the similar semantic meaning.Experiments are carried out on the Open Movie Database (OMDb), Reuters Dataset and 20NewsGroup Dataset and use the mean Average Precision score as the evaluation metric.Comparing our results with other state-of-the-art topic models, such as Latent Dirichlet allocation and traditional Restricted Boltzmann Machines.Our proposed data preprocessing can improve the generated topic accuracy by up to 12.99\\%.How the number of clusters and the number of word pairs should be adjusted for different type of text document is also discussed.", "target": ["Abbiamo proposto un metodo universale che può essere utilizzato nella fase di pre-elaborazione dei dati per generare il topic più significativo che rappresenta meglio il documento dato"]} +{"source": "Multi-task learning has been successful in modeling multiple related tasks with large, carefully curated labeled datasets.By leveraging the relationships among different tasks, multi-task learning framework can improve the performance significantly.However, most of the existing works are under the assumption that the predefined tasks are related to each other.Thus, their applications on real-world are limited, because rare real-world problems are closely related.Besides, the understanding of relationships among tasks has been ignored by most of the current methods.Along this line, we propose a novel multi-task learning framework - Learning To Transfer Via Modelling Multi-level Task Dependency, which constructed attention based dependency relationships among different tasks.At the same time, the dependency relationship can be used to guide what knowledge should be transferred, thus the performance of our model also be improved.To show the effectiveness of our model and the importance of considering multi-level dependency relationship, we conduct experiments on several public datasets, on which we obtain significant improvements over current methods.", "target": ["Proponiamo un nuovo framework di multi-task learning che estrae automaticamente la relazione di dipendenza multi-view e la usa per guidare il transfer della conoscenza tra diversi task."]} +{"source": "We design simple and quantifiable testing of global translation-invariance in deep learning models trained on the MNIST dataset.Experiments on convolutional and capsules neural networks show that both models have poor performance in dealing with global translation-invariance; however, the performance improved by using data augmentation.Although the capsule network is better on the MNIST testing dataset, the convolutional neural network generally has better performance on the translation-invariance.", "target": ["Verifica dell'invarianza traslazionale globale nelle convolutional e capsule network"]} +{"source": "Gaussian processes are ubiquitous in nature and engineering.A case in point is a class of neural networks in the infinite-width limit, whose priors correspond to Gaussian processes.Here we perturbatively extend this correspondence to finite-width neural networks, yielding non-Gaussian processes as priors.The methodology developed herein allows us to track the flow of preactivation distributions by progressively integrating out random variables from lower to higher layers, reminiscent of renormalization-group flow.We further develop a perturbative prescription to perform Bayesian inference with weakly non-Gaussian priors.", "target": ["Sviluppiamo un metodo analitico per studiare l'inferenza bayesiana delle reti neurali a larghezza finita e troviamo che l'immagine del flusso del gruppo di rinormalizzazione emerge naturalmente."]} +{"source": "Distillation is a method to transfer knowledge from one model to another and often achieves higher accuracy with the same capacity.In this paper, we aim to provide a theoretical understanding on what mainly helps with the distillation.Our answer is \"early stopping\".Assuming that the teacher network is overparameterized, we argue that the teacher network is essentially harvesting dark knowledge from the data via early stopping.This can be justified by a new concept, Anisotropic In- formation Retrieval (AIR), which means that the neural network tends to fit the informative information first and the non-informative information (including noise) later.Motivated by the recent development on theoretically analyzing overparame- terized neural networks, we can characterize AIR by the eigenspace of the Neural Tangent Kernel(NTK).AIR facilities a new understanding of distillation.With that, we further utilize distillation to refine noisy labels.We propose a self-distillation al- gorithm to sequentially distill knowledge from the network in the previous training epoch to avoid memorizing the wrong labels.We also demonstrate, both theoret- ically and empirically, that self-distillation can benefit from more than just early stopping.Theoretically, we prove convergence of the proposed algorithm to the ground truth labels for randomly initialized overparameterized neural networks in terms of l2 distance, while the previous result was on convergence in 0-1 loss.The theoretical result ensures the learned neural network enjoy a margin on the training data which leads to better generalization.Empirically, we achieve better testing accuracy and entirely avoid early stopping which makes the algorithm more user-friendly.", "target": ["capire teoricamente l'effetto di regolarizzazione della distillation. Mostriamo che l'early stopping è essenziale in questo processo. Da questa prospettiva, abbiamo sviluppato un metodo di distillation per l'apprendimento con label corrotte con garanzie teoriche."]} +{"source": "Lifelong learning poses considerable challenges in terms of effectiveness (minimizing prediction errors for all tasks) and overall computational tractability for real-time performance. This paper addresses continuous lifelong multitask learning by jointly re-estimating the inter-task relations (\\textit{output} kernel) and the per-task model parameters at each round, assuming data arrives in a streaming fashion.We propose a novel algorithm called \\textit{Online Output Kernel Learning Algorithm} (OOKLA) for lifelong learning setting.To avoid the memory explosion, we propose a robust budget-limited versions of the proposed algorithm that efficiently utilize the relationship between the tasks to bound the total number of representative examples in the support set. In addition, we propose a two-stage budgeted scheme for efficiently tackling the task-specific budget constraints in lifelong learning.Our empirical results over three datasets indicate superior AUC performance for OOKLA and its budget-limited cousins over strong baselines.", "target": ["un nuovo approccio per lifelong learning online utilizzando kernel di output."]} +{"source": "Minecraft is a videogame that offers many interesting challenges for AI systems.In this paper, we focus in construction scenarios where an agent must build a complex structure made of individual blocks.As higher-level objects are formed of lower-level objects, the construction can naturally be modelled as a hierarchical task network.We model a house-construction scenario in classical and HTN planning and compare the advantages and disadvantages of both kinds of models.", "target": ["Modelliamo uno scenario di costruzione di una casa in Minecraft in pianificazione classica e HTN e confrontiamo i vantaggi e gli svantaggi di entrambi i tipi di modelli."]} +{"source": "Attacks on natural language models are difficult to compare due to their different definitions of what constitutes a successful attack.We present a taxonomy of constraints to categorize these attacks.For each constraint, we present a real-world use case and a way to measure how well generated samples enforce the constraint.We then employ our framework to evaluate two state-of-the art attacks which fool models with synonym substitution.These attacks claim their adversarial perturbations preserve the semantics and syntactical correctness of the inputs, but our analysis shows these constraints are not strongly enforced.For a significant portion of these adversarial examples, a grammar checker detects an increase in errors.Additionally, human studies indicate that many of these adversarial examples diverge in semantic meaning from the input or do not appear to be human-written.Finally, we highlight the need for standardized evaluation of attacks that share constraints.Without shared evaluation metrics, it is up to researchers to set thresholds that determine the trade-off between attack quality and attack success.We recommend well-designed human studies to determine the best threshold to approximate human judgement.", "target": ["Presentiamo un framework per valutare gli adversarial example in NLP e dimostriamo che gli adversarial example generati spesso non preservano la semantica, e sono sintatticamente corretti o sospetti."]} +{"source": "In order for machine learning to be deployed and trusted in many applications, it is crucial to be able to reliably explain why the machine learning algorithm makes certain predictions.For example, if an algorithm classifies a given pathology image to be a malignant tumor, then the doctor may need to know which parts of the image led the algorithm to this classification.How to interpret black-box predictors is thus an important and active area of research. A fundamental question is: how much can we trust the interpretation itself?In this paper, we show that interpretation of deep learning predictions is extremely fragile in the following sense: two perceptively indistinguishable inputs with the same predicted label can be assigned very different}interpretations.We systematically characterize the fragility of the interpretations generated by several widely-used feature-importance interpretation methods (saliency maps, integrated gradient, and DeepLIFT) on ImageNet and CIFAR-10.Our experiments show that even small random perturbation can change the feature importance and new systematic perturbations can lead to dramatically different interpretations without changing the label.We extend these results to show that interpretations based on exemplars (e.g. influence functions) are similarly fragile.Our analysis of the geometry of the Hessian matrix gives insight on why fragility could be a fundamental challenge to the current interpretation approaches.", "target": ["Possiamo fidarci della spiegazione di una rete neurale per la sua predizione? Esaminiamo la robustezza di diverse nozioni popolari di interpretabilità delle reti neurali, comprese le mappe di saliency e le funzioni di influenza, e progettiamo adversarial example contro di esse."]} +{"source": "Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification.However, existing works are limited to stochastic AUC maximization with a linear predictive model, which restricts its predictive power when dealing with extremely complex data.In this paper, we consider stochastic AUC maximization problem with a deep neural network as the predictive model.Building on the saddle point reformulation of a surrogated loss of AUC, the problem can be cast into a {\\it non-convex concave} min-max problem.The main contribution made in this paper is to make stochastic AUC maximization more practical for deep neural networks and big data with theoretical insights as well.In particular, we propose to explore Polyak-\\L{}ojasiewicz (PL) condition that has been proved and observed in deep learning, which enables us to develop new stochastic algorithms with even faster convergence rate and more practical step size scheme.An AdaGrad-style algorithm is also analyzed under the PL condition with adaptive convergence rate.Our experimental results demonstrate the effectiveness of the proposed algorithms.", "target": ["L'articolo progetta due algoritmi per il problema di massimizzazione dell'AUC stocastico con complessità allo stato dell'arte quando si usa una deep neural network come modello predittivo, che sono anche verificati da studi empirici."]} +{"source": "Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute.The latter is particularly problematic when applying RL to robotics, where detecting whether the desired configuration is reached might require considerable supervision and instrumentation.Furthermore, we are often interested in being able to reach a wide range of configurations, hence setting up a different reward every time might be unpractical.Methods like Hindsight Experience Replay (HER) have recently shown promise to learn policies able to reach many goals, without the need of a reward.Unfortunately, without tricks like resetting to points along the trajectory, HER might take a very long time to discover how to reach certain areas of the state-space.In this work we investigate different approaches to incorporate demonstrations to drastically speed up the convergence to a policy able to reach any goal, also surpassing the performance of an agent trained with other Imitation Learning algorithms.Furthermore, our method can be used when only trajectories without expert actions are available, which can leverage kinestetic or third person demonstration.", "target": ["Affrontiamo task condizionati da obiettivi combinando gli algoritmi di Hindsight Experience Replay e di Imitation Learning, mostrando una convergenza più veloce del primo e prestazioni finali più elevate del secondo."]} +{"source": "Bayesian neural networks, which both use the negative log-likelihood loss function and average their predictions using a learned posterior over the parameters, have been used successfully across many scientific fields, partly due to their ability to `effortlessly' extract desired representations from many large-scale datasets.However, generalization bounds for this setting is still missing.In this paper, we present a new PAC-Bayesian generalization bound for the negative log-likelihood loss which utilizes the \\emph{Herbst Argument} for the log-Sobolev inequality to bound the moment generating function of the learners risk.", "target": ["Deriviamo un nuovo PAC-Bayesian Bound per loss function senza limiti (per esempio Negative Log-Likelihood)."]} +{"source": "Data augmentation techniques, e.g., flipping or cropping, which systematically enlarge the training dataset by explicitly generating more training samples, are effective in improving the generalization performance of deep neural networks.In the supervised setting, a common practice for data augmentation is to assign the same label to all augmented samples of the same source.However, if the augmentation results in large distributional discrepancy among them (e.g., rotations), forcing their label invariance may be too difficult to solve and often hurts the performance.To tackle this challenge, we suggest a simple yet effective idea of learning the joint distribution of the original and self-supervised labels of augmented samples.The joint learning framework is easier to train, and enables an aggregated inference combining the predictions from different augmented samples for improving the performance.Further, to speed up the aggregation process, we also propose a knowledge transfer technique, self-distillation, which transfers the knowledge of augmentation into the model itself.We demonstrate the effectiveness of our data augmentation framework on various fully-supervised settings including the few-shot and imbalanced classification scenarios.", "target": ["Proponiamo una semplice tecnica di data augmentation self-supervised che migliora le prestazioni degli scenari completamente supervisionati, tra cui l'apprendimento few shot e la classificazione imbalanced."]} +{"source": "Long short-term memory (LSTM) networks allow to exhibit temporal dynamic behavior with feedback connections and seem a natural choice for learning sequences of 3D meshes.We introduce an approach for dynamic mesh representations as used for numerical simulations of car crashes.To bypass the complication of using 3D meshes, we transform the surface mesh sequences into spectral descriptors that efficiently encode the shape.A two branch LSTM based network architecture is chosen to learn the representations and dynamics of the crash during the simulation.The architecture is based on unsupervised video prediction by an LSTM without any convolutional layer.It uses an encoder LSTM to map an input sequence into a fixed length vector representation.On this representation one decoder LSTM performs the reconstruction of the input sequence, while the other decoder LSTM predicts the future behavior by receiving initial steps of the sequence as seed.The spatio-temporal error behavior of the model is analysed to study how well the model can extrapolate the learned spectral descriptors into the future, that is, how well it has learned to represent the underlying dynamical structural mechanics.Considering that only a few training examples are available, which is the typical case for numerical simulations, the network performs very well.", "target": ["Un'architettura di rete basata su LSTM a due branch impara la rappresentazione e la dinamica delle mesh 3D delle simulazioni numeriche di crash."]} +{"source": "The purpose of an encoding model is to predict brain activity given a stimulus.In this contribution, we attempt at estimating a whole brain encoding model of auditory perception in a naturalistic stimulation setting.We analyze data from an open dataset, in which 16 subjects watched a short movie while their brain activity was being measured using functional MRI.We extracted feature vectors aligned with the timing of the audio from the movie, at different layers of a Deep Neural Network pretrained on the classification of auditory scenes.fMRI data was parcellated using hierarchical clustering on 500 parcels, and encoding models were estimated using a fully connected neural network with one hidden layer, trained to predict the signals for each parcel from the DNN features.Individual encoding models were successfully trained and predicted brain activity on unseen data, in parcels located in the superior temporal lobe, as well as dorsolateral prefrontal regions, which are usually considered as areas involved in auditory and language processing.Taken together, this contribution extends previous attempts on estimating encoding models, by showing the ability to model brain activity using a generic DNN (ie not specifically trained for this purpose) to extract auditory features, suggesting a degree of similarity between internal DNN representations and brain activity in naturalistic settings.", "target": ["I vettori di feature di SoundNet possono prevedere l'attività cerebrale dei soggetti che guardano un film nelle regioni cerebrali legate all'udito e al linguaggio."]} +{"source": "In this paper, we describe the \"implicit autoencoder\" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions.We use two generative adversarial networks to define the reconstruction and the regularization cost functions of the implicit autoencoder, and derive the learning rules based on maximum-likelihood learning.Using implicit distributions allows us to learn more expressive posterior and conditional likelihood distributions for the autoencoder.Learning an expressive conditional likelihood distribution enables the latent code to only capture the abstract and high-level information of the data, while the remaining information is captured by the implicit conditional likelihood distribution.For example, we show that implicit autoencoders can disentangle the global and local information, and perform deterministic or stochastic reconstructions of the images.We further show that implicit autoencoders can disentangle discrete underlying factors of variation from the continuous factors in an unsupervised fashion, and perform clustering and semi-supervised learning.", "target": ["Proponiamo un autoencoder generativo che può apprendere distribuzioni posteriori e di likelihood condizionale espressive usando distribuzioni implicite, e addestrare il modello usando una nuova formulazione dell'ELBO."]} +{"source": "Strong inductive biases allow children to learn in fast and adaptable ways.Children use the mutual exclusivity (ME) bias to help disambiguate how words map to referents, assuming that if an object has one label then it does not need another.In this paper, we investigate whether or not standard neural architectures have a ME bias, demonstrating that they lack this learning assumption.Moreover, we show that their inductive biases are poorly matched to lifelong learning formulations of classification and translation.We demonstrate that there is a compelling case for designing neural networks that reason by mutual exclusivity, which remains an open challenge.", "target": ["I bambini usano il bias di mutua esclusività (ME) per imparare nuove parole, mentre le reti neurali standard mostrano il bias opposto, ostacolando l'apprendimento in scenari naturalistici come lifelong learning."]} +{"source": "Cortical neurons process and integrate information on multiple timescales.In addition, these timescales or temporal receptive fields display functional and hierarchical organization.For instance, areas important for working memory (WM), such as prefrontal cortex, utilize neurons with stable temporal receptive fields and long timescales to support reliable representations of stimuli.Despite of the recent advances in experimental techniques, the underlying mechanisms for the emergence of neuronal timescales long enough to support WM are unclear and challenging to investigate experimentally.Here, we demonstrate that spiking recurrent neural networks (RNNs) designed to perform a WM task reproduce previously observed experimental findings and that these models could be utilized in the future to study how neuronal timescales specific to WM emerge.", "target": ["Le spiking recurrent neural network che eseguono un working memory task utilizzano lunghe scale temporali eterogenee, sorprendentemente simili a quelle osservate nella corteccia prefrontale."]} +{"source": "Conventional deep learning classifiers are static in the sense that they are trained ona predefined set of classes and learning to classify a novel class typically requiresre-training.In this work, we address the problem of Low-shot network-expansionlearning.We introduce a learning framework which enables expanding a pre-trained(base) deep network to classify novel classes when the number of examples for thenovel classes is particularly small.We present a simple yet powerful distillationmethod where the base network is augmented with additional weights to classifythe novel classes, while keeping the weights of the base network unchanged.Weterm this learning hard distillation, since we preserve the response of the networkon the old classes to be equal in both the base and the expanded network.Weshow that since only a small number of weights needs to be trained, the harddistillation excels for low-shot training scenarios.Furthermore, hard distillationavoids detriment to classification performance on the base classes.Finally, weshow that low-shot network expansion can be done with a very small memoryfootprint by using a compact generative model of the base classes training datawith only a negligible degradation relative to learning with the full training set.", "target": ["In questo articolo, affrontiamo il problema del network-expansion learning low-shot"]} +{"source": "People ask questions that are far richer, more informative, and more creative than current AI systems.We propose a neural program generation framework for modeling human question asking, which represents questions as formal programs and generates programs with an encoder-decoder based deep neural network.From extensive experiments using an information-search game, we show that our method can ask optimal questions in synthetic settings, and predict which questions humans are likely to ask in unconstrained settings.We also propose a novel grammar-based question generation framework trained with reinforcement learning, which is able to generate creative questions without supervised data.", "target": ["Introduciamo un modello di human question asking che combina reti neurali e programmi simbolici, che può imparare a generare buone domande con o senza esempi supervisionati."]} +{"source": "The classification of images taken in special imaging environments except air is the first challenge in extending the applications of deep learning.We report on an UW-Net (Underwater Network), a new convolutional neural network (CNN) based network for underwater image classification.In this model, we simulate the visual correlation of background attention with image understanding for special environments, such as fog and underwater by constructing an inception-attention (I-A) module.The experimental results demonstrate that the proposed UW-Net achieves an accuracy of 99.3% on underwater image classification, which is significantly better than other image classification networks, such as AlexNet, InceptionV3, ResNet and Se-ResNet.Moreover, we demonstrate the proposed IA module can be used to boost the performance of the existing object recognition networks.By substituting the inception module with the I-A module, the Inception-ResnetV2 network achieves a 10.7% top1 error rate and a 0% top5 error rate on the subset of ILSVRC-2012, which further illustrates the function of the background attention in the image classifications.", "target": ["Un meccanismo di comprensione visiva per ambienti speciali"]} +{"source": "Deep networks have achieved impressive results across a variety of important tasks.However, a known weakness is a failure to perform well when evaluated on data which differ from the training distribution, even if these differences are very small, as is the case with adversarial examples. We propose \\emph{Fortified Networks}, a simple transformation of existing networks, which “fortifies” the hidden layers in a deep network by identifying when the hidden states are off of the data manifold, and maps these hidden states back to parts of the data manifold where the network performs well.Our principal contribution is to show that fortifying these hidden states improves the robustness of deep networks and our experiments (i) demonstrate improved robustness to standard adversarial attacks in both black-box and white-box threat models; (ii) suggest that our improvements are not primarily due to the problem of deceptively good results due to degraded quality in the gradient signal (the gradient masking problem) and (iii) show the advantage of doing this fortification in the hidden layers instead of the input space. We demonstrate improvements in adversarial robustness on three datasets (MNIST, Fashion MNIST, CIFAR10), across several attack parameters, both white-box and black-box settings, and the most widely studied attacks (FGSM, PGD, Carlini-Wagner). We show that these improvements are achieved across a wide variety of hyperparameters.", "target": ["Un migliore adversarial training imparando a mappare il manifold dei dati con autoencoder negli hidden state."]} +{"source": "Neural networks could misclassify inputs that are slightly different from their training data, which indicates a small margin between their decision boundaries and the training dataset.In this work, we study the binary classification of linearly separable datasets and show that linear classifiers could also have decision boundaries that lie close to their training dataset if cross-entropy loss is used for training.In particular, we show that if the features of the training dataset lie in a low-dimensional affine subspace and the cross-entropy loss is minimized by using a gradient method, the margin between the training points and the decision boundary could be much smaller than the optimal value.This result is contrary to the conclusions of recent related works such as (Soudry et al., 2018), and we identify the reason for this contradiction.In order to improve the margin, we introduce differential training, which is a training paradigm that uses a loss function defined on pairs of points from each class.We show that the decision boundary of a linear classifier trained with differential training indeed achieves the maximum margin.The results reveal the use of cross-entropy loss as one of the hidden culprits of adversarial examples and introduces a new direction to make neural networks robust against them.", "target": ["Mostriamo che la minimizzazione della cross-entropy loss utilizzando un metodo con gradiente potrebbe portare a un margine molto povero se le feature del dataset si trovano su un sottospazio a bassa dimensionalità."]} +{"source": "The concepts of unitary evolution matrices and associative memory have boosted the field of Recurrent Neural Networks (RNN) to state-of-the-art performance in a variety of sequential tasks. However, RNN still has a limited capacity to manipulate long-term memory. To bypass this weakness the most successful applications of RNN use external techniques such as attention mechanisms.In this paper we propose a novel RNN model that unifies the state-of-the-art approaches: Rotational Unit of Memory (RUM).The core of RUM is its rotational operation, which is, naturally, a unitary matrix, providing architectures with the power to learn long-term dependencies by overcoming the vanishing and exploding gradients problem. Moreover, the rotational unit also serves as associative memory.We evaluate our model on synthetic memorization, question answering and language modeling tasks. RUM learns the Copying Memory task completely and improves the state-of-the-art result in the Recall task. RUM’s performance in the bAbI Question Answering task is comparable to that of models with attention mechanism.We also improve the state-of-the-art result to 1.189 bits-per-character (BPC) loss in the Character Level Penn Treebank (PTB) task, which is to signify the applications of RUM to real-world sequential data.The universality of our construction, at the core of RNN, establishes RUM as a promising approach to language modeling, speech recognition and machine translation.", "target": ["Un nuovo modello RNN che supera significativamente l'attuale frontiera dei modelli in una varietà di task sequenziali."]} +{"source": "While many recent advances in deep reinforcement learning rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment dynamics.One prospect is to pursue hybrid approaches, as in AlphaGo, which combines Monte-Carlo Tree Search (MCTS)—a model-based method—with deep-Q networks (DQNs)—a model-free method.MCTS requires generating rollouts, which is computationally expensive.In this paper, we propose to simulate roll-outs, exploiting the latest breakthroughs in image-to-image transduction, namely Pix2Pix GANs, to predict the dynamics of the environment.Our proposed algorithm, generative adversarial tree search (GATS), simulates rollouts up to a specified depth using both a GAN- based dynamics model and a reward predictor.GATS employs MCTS for planning over the simulated samples and uses DQN to estimate the Q-function at the leaf states.Our theoretical analysis establishes some favorable properties of GATS vis-a-vis the bias-variance trade-off and empirical results show that on 5 popular Atari games, the dynamics and reward predictors converge quickly to accurate solutions.However, GATS fails to outperform DQNs in 4 out of 5 games.Notably, in these experiments, MCTS has only short rollouts (up to tree depth 4), while previous successes of MCTS have involved tree depth in the hundreds.We present a hypothesis for why tree search with short rollouts can fail even given perfect modeling.", "target": ["Sorprendenti risultati negativi su Model Based + Model deep RL"]} +{"source": "We present a novel architecture of GAN for a disentangled representation learning.The new model architecture is inspired by Information Bottleneck (IB) theory thereby named IB-GAN.IB-GAN objective is similar to that of InfoGAN but has a crucial difference; a capacity regularization for mutual information is adopted, thanks to which the generator of IB-GAN can harness a latent representation in disentangled and interpretable manner.To facilitate the optimization of IB-GAN in practice, a new variational upper-bound is derived.With experiments on CelebA, 3DChairs, and dSprites datasets, we demonstrate that the visual quality of samples generated by IB-GAN is often better than those by β-VAEs.Moreover, IB-GAN achieves much higher disentanglement metrics score than β-VAEs or InfoGAN on the dSprites dataset.", "target": ["Ispirati dalla teoria dell'Information Bottleneck, proponiamo una nuova architettura di GAN per l'apprendimento di una rappresentazione disentangled"]} +{"source": "We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs.As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning a discriminator based on samples leads to biased gradients for the generator parameters.We also discuss the issue of kernel choice for the MMD critic, and characterize the kernel corresponding to the energy distance used for the Cramér GAN critic.Being an integral probability metric, the MMD benefits from training strategies recently developed for Wasserstein GANs.In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance.We also propose an improved measure of GAN convergence, the Kernel Inception Distance, and show how to use it to dynamically adapt learning rates during GAN training.", "target": ["Spiegare la situazione di bias con le GAN MMD; le GAN MMD funzionano con reti critiche più piccole delle WGAN-GP; nuova metrica di valutazione delle GAN."]} +{"source": "We extend the Consensus Network framework to Transductive Consensus Network (TCN), a semi-supervised multi-modal classification framework, and identify its two mechanisms: consensus and classification.By putting forward three variants as ablation studies, we show both mechanisms should be functioning together.Overall, TCNs outperform or align with the best benchmark algorithms when only 20 to 200 labeled data points are available.", "target": ["Un framework di classificazione multimodale semi-supervised, TCN, che supera vari benchmark."]} +{"source": "Separating mixed distributions is a long standing challenge for machine learning and signal processing.Applications include: single-channel multi-speaker separation (cocktail party problem), singing voice separation and separating reflections from images.Most current methods either rely on making strong assumptions on the source distributions (e.g. sparsity, low rank, repetitiveness) or rely on having training samples of each source in the mixture.In this work, we tackle the scenario of extracting an unobserved distribution additively mixed with a signal from an observed (arbitrary) distribution.We introduce a new method: Neural Egg Separation - an iterative method that learns to separate the known distribution from progressively finer estimates of the unknown distribution.In some settings, Neural Egg Separation is initialization sensitive, we therefore introduce GLO Masking which ensures a good initialization.Extensive experiments show that our method outperforms current methods that use the same level of supervision and often achieves similar performance to full supervision.", "target": ["Un metodo neurale iterativo per l'estrazione di segnali che vengono osservati solo quando mescolati ad altri segnali"]} +{"source": "In health, machine learning is increasingly common, yet neural network embedding (representation) learning is arguably under-utilized for physiological signals. This inadequacy stands out in stark contrast to more traditional computer science domains, such as computer vision (CV), and natural language processing (NLP). For physiological signals, learning feature embeddings is a natural solution to data insufficiency caused by patient privacy concerns -- rather than share data, researchers may share informative embedding models (i.e., representation models), which map patient data to an output embedding. Here, we present the PHASE (PHysiologicAl Signal Embeddings) framework, which consists of three components: i) learning neural network embeddings of physiological signals, ii) predicting outcomes based on the learned embedding, and iii) interpreting the prediction results by estimating feature attributions in the \"stacked\" models (i.e., feature embedding model followed by prediction model). PHASE is novel in three ways: 1) To our knowledge, PHASE is the first instance of transferal of neural networks to create physiological signal embeddings.2) We present a tractable method to obtain feature attributions through stacked models. We prove that our stacked model attributions can approximate Shapley values -- attributions known to have desirable properties -- for arbitrary sets of models.3) PHASE was extensively tested in a cross-hospital setting including publicly available data. In our experiments, we show that PHASE significantly outperforms alternative embeddings -- such as raw, exponential moving average/variance, and autoencoder -- currently in use.Furthermore, we provide evidence that transferring neural network embedding/representation learners between distinct hospitals still yields performant embeddings and offer recommendations when transference is ineffective.", "target": ["Embedding del segnale fisiologico per le prestazioni di predizione e hospital transference con un metodo generale di interpretabilità del valore di Shapley per i modelli stacked."]} +{"source": "We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse weights forming the linear combination are known as coefficients.Since the dictionary and coefficients, parameterizing the linear model are unknown, the corresponding optimization is inherently non-convex.This was a major challenge until recently, when provable algorithms for dictionary learning were proposed.Yet, these provide guarantees only on the recovery of the dictionary, without explicit recovery guarantees on the coefficients.Moreover, any estimation error in the dictionary adversely impacts the ability to successfully localize and estimate the coefficients.This potentially limits the utility of existing provable dictionary learning methods in applications where coefficient recovery is of interest.To this end, we develop NOODL: a simple Neurally plausible alternating Optimization-based Online Dictionary Learning algorithm, which recovers both the dictionary and coefficients exactly at a geometric rate, when initialized appropriately.Our algorithm, NOODL, is also scalable and amenable for large scale distributed implementations in neural architectures, by which we mean that it only involves simple linear and non-linear operations.Finally, we corroborate these theoretical results via experimental evaluation of the proposed algorithm with the current state-of-the-art techniques.", "target": ["Presentiamo un algoritmo dimostrabile per recuperare esattamente entrambi i fattori del modello di apprendimento del dizionario."]} +{"source": "Real-world Relation Extraction (RE) tasks are challenging to deal with, either due to limited training data or class imbalance issues.In this work, we present Data Augmented Relation Extraction (DARE), a simple method to augment training data by properly finetuning GPT2 to generate examples for specific relation types.The generated training data is then used in combination with the gold dataset to train a BERT-based RE classifier.In a series of experiments we show the advantages of our method, which leads in improvements of up to 11 F1 score points compared to a strong baseline.Also, DARE achieves new state-of-the-art in three widely used biomedical RE datasets surpassing the previous best results by 4.7 F1 points on average.", "target": ["Estrazione di relazioni con data augmentation tramite GPT-2"]} +{"source": "This paper addresses the problem of representing a system's belief using multi-variate normal distributions (MND) where the underlying model is based on a deep neural network (DNN).The major challenge with DNNs is the computational complexity that is needed to obtain model uncertainty using MNDs.To achieve a scalable method, we propose a novel approach that expresses the parameter posterior in sparse information form.Our inference algorithm is based on a novel Laplace Approximation scheme, which involves a diagonal correction of the Kronecker-factored eigenbasis.As this makes the inversion of the information matrix intractable - an operation that is required for full Bayesian analysis, we devise a low-rank approximation of this eigenbasis and a memory-efficient sampling scheme.We provide both a theoretical analysis and an empirical evaluation on various benchmark data sets, showing the superiority of our approach over existing methods.", "target": ["Un algoritmo di inferenza approssimato per deep learning"]} +{"source": "The ever-increasing size of modern datasets combined with the difficulty of obtaining label information has made semi-supervised learning of significant practical importance in modern machine learning applications.In comparison to supervised learning, the key difficulty in semi-supervised learning is how to make full use of the unlabeled data.In order to utilize manifold information provided by unlabeled data, we propose a novel regularization called the tangent-normal adversarial regularization, which is composed by two parts.The two parts complement with each other and jointly enforce the smoothness along two different directions that are crucial for semi-supervised learning.One is applied along the tangent space of the data manifold, aiming to enforce local invariance of the classifier on the manifold, while the other is performed on the normal space orthogonal to the tangent space, intending to impose robustness on the classifier against the noise causing the observed data deviating from the underlying data manifold. Both of the two regularizers are achieved by the strategy of virtual adversarial training.Our method has achieved state-of-the-art performance on semi-supervised learning tasks on both artificial dataset and practical datasets.", "target": ["Proponiamo una nuova strategia di regolarizzazione del manifold basata su adversarial training, che può migliorare significativamente le prestazioni dell'apprendimento semi-supervised."]} +{"source": "Universal approximation property of neural networks is one of the motivations to use these models in various real-world problems.However, this property is not the only characteristic that makes neural networks unique as there is a wide range of other approaches with similar property.Another characteristic which makes these models interesting is that they can be trained with the backpropagation algorithm which allows an efficient gradient computation and gives these universal approximators the ability to efficiently learn complex manifolds from a large amount of data in different domains.Despite their abundant use in practice, neural networks are still not well understood and a broad range of ongoing research is to study the interpretability of neural networks.On the other hand, topological data analysis (TDA) relies on strong theoretical framework of (algebraic) topology along with other mathematical tools for analyzing possibly complex datasets.In this work, we leverage a universal approximation theorem originating from algebraic topology to build a connection between TDA and common neural network training framework.We introduce the notion of automatic subdivisioning and devise a particular type of neural networks for regression tasks: Simplicial Complex Networks (SCNs).SCN's architecture is defined with a set of bias functions along with a particular policy during the forward pass which alternates the common architecture search framework in neural networks.We believe the view of SCNs can be used as a step towards building interpretable deep learning models.Finally, we verify its performance on a set of regression problems.", "target": ["Un nuovo metodo per l'apprendimento supervised attraverso la suddivisione dello spazio di input insieme all'approssimazione della funzione."]} +{"source": "The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks.However, despite the proliferation of such methods there is currently no study of their robustness to adversarial attacks.We provide the first adversarial vulnerability analysis on the widely used family of methods based on random walks.We derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks.We further show that our attacks are transferable since they generalize to many models, and are successful even when the attacker is restricted.", "target": ["Adversarial attack su embedding di nodi non supervisionati basati sulla teoria della perturbazione degli autovalori."]} +{"source": "Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains.However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge.We present a novel algorithm for subtask discovery, based on the recently introduced multitask linearly-solvable Markov decision process (MLMDP) framework.The MLMDP can perform never-before-seen tasks by representing them as a linear combination of a previously learned basis set of tasks.In this setting, the subtask discovery problem can naturally be posed as finding an optimal low-rank approximation of the set of tasks the agent will face in a domain.We use non-negative matrix factorization to discover this minimal basis set of tasks, and show that the technique learns intuitive decompositions in a variety of domains.Our method has several qualitatively desirable features: it is not limited to learning subtasks with single goal states, instead learning distributed patterns of preferred states; it learns qualitatively different hierarchical decompositions in the same domain depending on the ensemble of tasks the agent will face; and it may be straightforwardly iterated to obtain deeper hierarchical decompositions.", "target": ["Presentiamo un nuovo algoritmo per la scoperta gerarchica di subtask che sfrutta il framework del processo decisionale lineare di Markov multitask."]} +{"source": "This paper introduces a framework for solving combinatorial optimization problems by learning from input-output examples of optimization problems.We introduce a new memory augmented neural model in which the memory is not resettable (i.e the information stored in the memory after processing an input example is kept for the next seen examples).We used deep reinforcement learning to train a memory controller agent to store useful memories.Our model was able to outperform hand-crafted solver on Binary Linear Programming (Binary LP).The proposed model is tested on different Binary LP instances with large number of variables (up to 1000 variables) and constrains (up to 700 constrains).", "target": ["Proponiamo un modello di memory network per risolvere istanze LP binarie in cui l'informazione della memoria è conservata per l'uso a lungo termine."]} +{"source": "Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition.Performance has further been improved by leveraging unlabeled data, often in the form of a language model.In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task.We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoyingi) faster convergence and better generalization, andii) almost complete transfer to a new domain while using less than 10% of the labeled training data.", "target": ["Introduciamo un nuovo metodo per addestrare modelli Seq2Seq con language model che convergono più velocemente, generalizzano meglio e possono trasferirsi quasi completamente a un nuovo dominio usando meno del 10% di dati annotati."]} +{"source": "A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks.Two distinct research paradigms have studied this question.Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch.In contrast, online (regret based) learning considers a sequential setting in which problems are revealed one after the other, but conventionally train only a single model without any task-specific adaptation.This work introduces an online meta-learning setting, which merges ideas from both the aforementioned paradigms to better capture the spirit and practice of continual lifelong learning.We propose the follow the meta leader (FTML) algorithm which extends the MAML algorithm to this setting.Theoretically, this work provides an O(logT) regret guarantee for the FTML algorithm.Our experimental evaluation on three different large-scale tasks suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches.", "target": ["Introduciamo il setting del problema del meta learning online per catturare meglio lo spirito e la pratica del continual lifelong learning."]} +{"source": "We investigate the extent to which individual attention heads in pretrained transformer language models, such as BERT and RoBERTa, implicitly capture syntactic dependency relations.We employ two methods—taking the maximum attention weight and computing the maximum spanning tree—to extract implicit dependency relations from the attention weights of each layer/head, and compare them to the ground-truth Universal Dependency (UD) trees.We show that, for some UD relation types, there exist heads that can recover the dependency type significantly better than baselines on parsed English text, suggesting that some self-attention heads act as a proxy for syntactic structure.We also analyze BERT fine-tuned on two datasets—the syntax-oriented CoLA and the semantics-oriented MNLI—to investigate whether fine-tuning affects the patterns of their self-attention, but we do not observe substantial differences in the overall dependency relations extracted using our methods.Our results suggest that these models have some specialist attention heads that track individual dependency types, but no generalist head that performs holistic parsing significantly better than a trivial baseline, and that analyzing attention weights directly may not reveal much of the syntactic knowledge that BERT-style models are known to learn.", "target": ["I pesi di attention non espongono completamente ciò che BERT conosce della sintassi."]} +{"source": "State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high-resolution facial patterns by exploring local appearance knowledge.However, most of these methods do not well exploit facial structures and identity information, and struggle to deal with facial images that exhibit large pose variation and misalignment.In this paper, we propose a novel face super-resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures.Firstly, the 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge.Secondly, the Spatial Attention Mechanism is used to better exploit this hierarchical information (i.e. intensity similarity, 3D facial structure, identity content) for the super-resolution problem.Extensive experiments demonstrate that the proposed algorithm achieves superior face super-resolution results and outperforms the state-of-the-art.", "target": ["Proponiamo un nuovo metodo di super risoluzione del viso che incorpora esplicitamente prior facciali 3D che afferrano le strutture facciali nitide."]} +{"source": "We present Cross-View Training (CVT), a simple but effective method for deep semi-supervised learning.On labeled examples, the model is trained with standard cross-entropy loss.On an unlabeled example, the model first performs inference (acting as a \"teacher\") to produce soft targets.The model then learns from these soft targets (acting as a ``\"student\").We deviate from prior work by adding multiple auxiliary student prediction layers to the model.The input to each student layer is a sub-network of the full model that has a restricted view of the input (e.g., only seeing one region of an image).The students can learn from the teacher (the full model) because the teacher sees more of each example.Concurrently, the students improve the quality of the representations used by the teacher as they learn to make predictions with limited data.When combined with Virtual Adversarial Training, CVT improves upon the current state-of-the-art on semi-supervised CIFAR-10 and semi-supervised SVHN.We also apply CVT to train models on five natural language processing tasks using hundreds of millions of sentences of unlabeled data.On all tasks CVT substantially outperforms supervised learning alone, resulting in models that improve upon or are competitive with the current state-of-the-art.", "target": ["Il self-training con diversi punti di vista dell'input dà risultati eccellenti per il riconoscimento semi-supervised delle immagini, sequence tagging e dependency parsing."]} +{"source": "I show how it can be beneficial to express Metropolis accept/reject decisions in terms of comparison with a uniform [0,1] value, and to then update this uniform value non-reversibly, as part of the Markov chain state, rather than sampling it independently each iteration.This provides a small improvement for random walk Metropolis and Langevin updates in high dimensions. It produces a larger improvement when using Langevin updates with persistent momentum, giving performance comparable to that of Hamiltonian Monte Carlo (HMC) with long trajectories. This is of significance when some variables are updated by other methods, since if HMC is used, these updates can be done only between trajectories, whereas they can be done more often with Langevin updates. This is seen for a Bayesian neural network model, in which connection weights are updated by persistent Langevin or HMC, while hyperparameters are updated by Gibbs sampling.", "target": ["Un modo non reversibile di prendere decisioni di accept/reject può essere vantaggioso"]} +{"source": "We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES).Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies.We show how ES can be applied to MAML to obtain an algorithm which avoids the problem of estimating second derivatives, and is also conceptually simple and easy to implement.Moreover, ES-MAML can handle new types of nonsmooth adaptation operators, and other techniques for improving performance and estimation of ES methods become applicable.We show empirically that ES-MAML is competitive with existing methods and often yields better adaptation with fewer queries.", "target": ["Forniamo un nuovo framework per MAML nel setting ES/blackbox, e mostriamo che permette policy deterministiche e lineari, una migliore esplorazione e operatori di adattamento non differenziabili."]} +{"source": "Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning.Some prominent examples are constrained-to-unconstrained transformations of distributions for use in Hamiltonian Monte-Carlo and constructing flexible and learnable densities such as normalizing flows.We present Bijectors.jl, a software package for transforming distributions implemented in Julia, available at github.com/TuringLang/Bijectors.jl.The package provides a flexible and composable way of implementing transformations of distributions without being tied to a computational framework. We demonstrate the use of Bijectors.jl on improving variational inference by encoding known statistical dependencies into the variational posterior using normalizing flows, providing a general approach to relaxing the mean-field assumption usually made in variational inference.", "target": ["Presentiamo un software framework per la trasformazione delle distribuzioni e dimostriamo la sua flessibilità nel rilassare le ipotesi di mean-field nell'inferenza variazionale con l'uso di flussi di accoppiamento per replicare la struttura dal modello generativo target."]} +{"source": "Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains.Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant experimental bias.This paper presents a multi-domain adversarial learning approach, MuLANN, to leverage multiple datasets with overlapping but distinct class sets, in a semi-supervised setting.Our contributions include:i) a bound on the average- and worst-domain risk in MDL, obtained using the H-divergence;ii) a new loss to accommodate semi-supervised multi-domain learning and domain adaptation;iii) the experimental validation of the approach, improving on the state of the art on two standard image benchmarks, and a novel bioimage dataset, Cell.", "target": ["Adversarial domain adaptation e multi-domain learning: una nuova loss per gestire classi multi-domain e single-domain nel setting semi-supervised."]} +{"source": "We introduce our Distribution Regression Network (DRN) which performs regression from input probability distributions to output probability distributions.Compared to existing methods, DRN learns with fewer model parameters and easily extends to multiple input and multiple output distributions.On synthetic and real-world datasets, DRN performs similarly or better than the state-of-the-art.Furthermore, DRN generalizes the conventional multilayer perceptron (MLP).In the framework of MLP, each node encodes a real number, whereas in DRN, each node encodes a probability distribution.", "target": ["Una rete di apprendimento che generalizza la struttura MLP per eseguire la regressione distribution-to-distribution."]} +{"source": "Existing sequence prediction methods are mostly concerned with time-independent sequences, in which the actual time span between events is irrelevant and the distance between events is simply the difference between their order positions in the sequence.While this time-independent view of sequences is applicable for data such as natural languages, e.g., dealing with words in a sentence, it is inappropriate and inefficient for many real world events that are observed and collected at unequally spaced points of time as they naturally arise, e.g., when a person goes to a grocery store or makes a phone call.The time span between events can carry important information about the sequence dependence of human behaviors.In this work, we propose a set of methods for using time in sequence prediction.Because neural sequence models such as RNN are more amenable for handling token-like input, we propose two methods for time-dependent event representation, based on the intuition on how time is tokenized in everyday life and previous work on embedding contextualization.We also introduce two methods for using next event duration as regularization for training a sequence prediction model.We discuss these methods based on recurrent neural nets.We evaluate these methods as well as baseline models on five datasets that resemble a variety of sequence prediction tasks.The experiments revealed that the proposed methods offer accuracy gain over baseline models in a range of settings.", "target": ["Propone metodi per la rappresentazione di eventi dipendenti dal tempo e la regolarizzazione per la predizione di sequenze; valuta questi metodi su cinque dataset che coinvolgono una serie di task di predizione di sequenze."]} +{"source": "Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience can be acquired.This property makes these algorithms appealing for real world problems such as robot control.In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.In this paper, we propose a simple solution to this problem.It admits the use of data generated by arbitrary behavior policies and uses a learned prior -- the advantage-weighted behavior model (ABM) -- to bias the RL policy towards actions that have previously been executed and are likely to be successful on the new task.Our method can be seen as an extension of recent work on batch-RL that enables stable learning from conflicting data-sources.We find improvements on competitive baselines in a variety of RL tasks -- including standard continuous control benchmarks and multi-task learning for simulated and real-world robots.", "target": ["Sviluppiamo un metodo per un reinforcement learning offline stabile a partire dai dati loggati. La chiave è regolarizzare la policy RL verso un modello \"advantage weighted\" dei dati."]} +{"source": "One of the main challenges in applying graph convolutional neural networks on gene-interaction data is the lack of understanding of the vector space to which they belong and also the inherent difficulties involved in representing those interactions on a significantly lower dimension, viz Euclidean spaces.The challenge becomes more prevalent when dealing with various types of heterogeneous data.We introduce a systematic, generalized method, called iSOM-GSN, used to transform ``multi-omic'' data with higher dimensions onto a two-dimensional grid.Afterwards, we apply a convolutional neural network to predict disease states of various types.Based on the idea of Kohonen's self-organizing map, we generate a two-dimensional grid for each sample for a given set of genes that represent a gene similarity network. We have tested the model to predict breast and prostate cancer using gene expression, DNA methylation and copy number alteration, yielding prediction accuracies in the 94-98% range for tumor stages of breast cancer and calculated Gleason scores of prostate cancer with just 11 input genes for both cases.The scheme not only outputs nearly perfect classification accuracy, but also provides an enhanced scheme for representation learning, visualization, dimensionality reduction, and interpretation of the results.", "target": ["Questo articolo presenta un modello di deep learning che combina mappe self-organizing e reti neurali convoluzionali per representation learning dei dati multi-omics"]} +{"source": "State-of-the-art performances on language comprehension tasks are achieved by huge language models pre-trained on massive unlabeled text corpora, with very light subsequent fine-tuning in a task-specific supervised manner.It seems the pre-training procedure learns a very good common initialization for further training on various natural language understanding tasks, such that only few steps need to be taken in the parameter space to learn each task.In this work, using Bidirectional Encoder Representations from Transformers (BERT) as an example, we verify this hypothesis by showing that task-specific fine-tuned language models are highly close in parameter space to the pre-trained one.Taking advantage of such observations, we further show that the fine-tuned versions of these huge models, having on the order of $10^8$ floating-point parameters, can be made very computationally efficient.First, fine-tuning only a fraction of critical layers suffices.Second, fine-tuning can be adequately performed by learning a binary multiplicative mask on pre-trained weights, \\textit{i.e.} by parameter-sparsification.As a result, with a single effort, we achieve three desired outcomes: (1) learning to perform specific tasks, (2) saving memory by storing only binary masks of certain layers for each task, and (3) saving compute on appropriate hardware by performing sparse operations with model parameters.", "target": ["Sparsificazione come fine-tuning dei language model"]} +{"source": "We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning.It operates by training an ensemble of policies on the expert demonstration data, and using the variance of their predictions as a cost which is minimized with RL together with a supervised behavioral cloning cost.Unlike adversarial imitation methods, it uses a fixed reward function which is easy to optimize.We prove a regret bound for the algorithm in the tabular setting which is linear in the time horizon multiplied by a coefficient which we show to be low for certain problems in which behavioral cloning fails.We evaluate our algorithm empirically across multiple pixel-based Atari environments and continuous control tasks, and show that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning.", "target": ["Metodo per affrontare il covariate shift nell'imitation learning usando l'incertezza dell'ensemble"]} +{"source": "We present and discuss a simple image preprocessing method for learning disentangled latent factors. In particular, we utilize the implicit inductive bias contained in features from networks pretrained on the ImageNet database. We enhance this bias by explicitly fine-tuning such pretrained networks on tasks useful for the NeurIPS2019 disentanglement challenge, such as angle and position estimation or color classification.Furthermore, we train a VAE on regionally aggregate feature maps, and discuss its disentanglement performance using metrics proposed in recent literature.", "target": ["Usiamo il finetuning supervised dei vettori di feature per migliorare il transfer dalla simulazione al mondo reale"]} +{"source": "A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy.In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals.In sparse-reward environments, the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended appears crucial to enable sample efficient learning.However, reinforcement learning agents have only recently been endowed with such capacity for hindsight.In this paper, we demonstrate how hindsight can be introduced to policy gradient methods, generalizing this idea to a broad class of successful algorithms.Our experiments on a diverse selection of sparse-reward environments show that hindsight leads to a remarkable increase in sample efficiency.", "target": ["Introduciamo la capacità di sfruttare le informazioni sul grado in cui un obiettivo arbitrario è stato raggiunto mentre un altro obiettivo era destinato ai metodi policy gradient."]} +{"source": "Computer vision has undergone a dramatic revolution in performance, driven in large part through deep features trained on large-scale supervised datasets.However, much of these improvements have focused on static image analysis; video understanding has seen rather modest improvements.Even though new datasets and spatiotemporal models have been proposed, simple frame-by-frame classification methods often still remain competitive.We posit that current video datasets are plagued with implicit biases over scene and object structure that can dwarf variations in temporal structure.In this work, we build a video dataset with fully observable and controllable object and scene bias, and which truly requires spatiotemporal understanding in order to be solved.Our dataset, named CATER, is rendered synthetically using a library of standard 3D objects, and tests the ability to recognize compositions of object movements that require long-term reasoning.In addition to being a challenging dataset, CATER also provides a plethora of diagnostic tools to analyze modern spatiotemporal video architectures by being completely observable and controllable.Using CATER, we provide insights into some of the most recent state of the art deep video architectures.", "target": ["Proponiamo un nuovo benchmark per la comprensione dei video, con task che per progettazione richiedono un ragionamento temporale per essere risolti, a differenza della maggior parte dei dataset video esistenti."]} +{"source": "We address the efficiency issues caused by the straggler effect in the recently emerged federated learning, which collaboratively trains a model on decentralized non-i.i.d. (non-independent and identically distributed) data across massive worker devices without exchanging training data in the unreliable and heterogeneous networks.We propose a novel two-stage analysis on the error bounds of general federated learning, which provides practical insights into optimization.As a result, we propose a novel easy-to-implement federated learning algorithm that uses asynchronous settings and strategies to control discrepancies between the global model and delayed models and adjust the number of local epochs with the estimation of staleness to accelerate convergence and resist performance deterioration caused by stragglers.Experiment results show that our algorithm converges fast and robust on the existence of massive stragglers.", "target": ["Proponiamo un algoritmo efficiente e robusto di federated learning asincrono sull'esistenza degli stragglers"]} +{"source": "Long Short-Term Memory (LSTM) units have the ability to memorise and use long-term dependencies between inputs to generate predictions on time series data.We introduce the concept of modifying the cell state (memory) of LSTMs using rotation matrices parametrised by a new set of trainable weights.This addition shows significant increases of performance on some of the tasks from the bAbI dataset.", "target": ["L'aggiunta di un nuovo set di pesi alla LSTM che ruota la memoria della cell migliora le prestazioni su alcuni task bAbI."]} +{"source": "We address the problem of marginal inference for an exponential family defined over the set of permutation matrices.This problem is known to quickly become intractable as the size of the permutation increases, since its involves the computation of the permanent of a matrix, a #P-hard problem.We introduce Sinkhorn variational marginal inference as a scalable alternative, a method whose validity is ultimately justified by the so-called Sinkhorn approximation of the permanent.We demonstrate the efectiveness of our method in the problem of probabilistic identification of neurons in the worm C.elegans", "target": ["Nuova metodologia per l'inferenza marginale variazionale delle permutazioni basata sull'algoritmo di Sinkhorn, applicata all'identificazione probabilistica dei neuroni"]} +{"source": "The robustness of neural networks to adversarial examples has received great attention due to security implications.Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness.In this paper, we provide theoretical justification for converting robustness analysis into a local Lipschitz constant estimation problem, and propose to use the Extreme Value Theory for efficient evaluation.Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness.The proposed CLEVER score is attack-agnostic and is computationally feasible for large neural networks.Experimental results on various networks, including ResNet, Inception-v3 and MobileNet, show that(i) CLEVER is aligned with the robustness indication measured by the $\\ell_2$ and $\\ell_\\infty$ norms of adversarial examples from powerful attacks, and(ii) defended networks using defensive distillation or bounded ReLU indeed give better CLEVER scores.To the best of our knowledge, CLEVER is the first attack-independent robustness metric that can be applied to any neural network classifiers.", "target": ["Proponiamo la prima metrica di robustezza indipendente dagli attacchi, nota come CLEVER, che può essere applicata a qualsiasi classificatore basato rete neurale."]} +{"source": "Multi-agent collaboration is required by numerous real-world problems.Although distributed setting is usually adopted by practical systems, local range communication and information aggregation still matter in fulfilling complex tasks.For multi-agent reinforcement learning, many previous studies have been dedicated to design an effective communication architecture.However, existing models usually suffer from an ossified communication structure, e.g., most of them predefine a particular communication mode by specifying a fixed time frequency and spatial scope for agents to communicate regardless of necessity.Such design is incapable of dealing with multi-agent scenarios that are capricious and complicated, especially when only partial information is available.Motivated by this, we argue that the solution is to build a spontaneous and self-organizing communication (SSoC) learning scheme.By treating the communication behaviour as an explicit action, SSoC learns to organize communication in an effective and efficient way.Particularly, it enables each agent to spontaneously decide when and who to send messages based on its observed states.In this way, a dynamic inter-agent communication channel is established in an online and self-organizing manner.The agents also learn how to adaptively aggregate the received messages and its own hidden states to execute actions.Various experiments have been conducted to demonstrate that SSoC really learns intelligent message passing among agents located far apart.With such agile communications, we observe that effective collaboration tactics emerge which have not been mastered by the compared baselines.", "target": ["Questo articolo propone uno schema di apprendimento spontaneo e auto-organizzante della comunicazione (SSoC) per task RL multi-agente."]} +{"source": "We study the BERT language representation model and the sequence generation model with BERT encoder for multi-label text classification task.We experiment with both models and explore their special qualities for this setting.We also introduce and examine experimentally a mixed model, which is an ensemble of multi-label BERT and sequence generating BERT models.Our experiments demonstrated that BERT-based models and the mixed model, in particular, outperform current baselines in several metrics achieving state-of-the-art results on three well-studied multi-label classification datasets with English texts and two private Yandex Taxi datasets with Russian texts.", "target": ["Sull'uso di BERT come encoder per la predizione sequenziale delle label nel task di text classification mulit-label"]} +{"source": "Click Through Rate (CTR) prediction is a critical task in industrial applications, especially for online social and commerce applications.It is challenging to find a proper way to automatically discover the effective cross features in CTR tasks.We propose a novel model for CTR tasks, called Deep neural networks with Encoder enhanced Factorization Machine (DeepEnFM).Instead of learning the cross features directly, DeepEnFM adopts the Transformer encoder as a backbone to align the feature embeddings with the clues of other fields.The embeddings generated from encoder are beneficial for the further feature interactions.Particularly, DeepEnFM utilizes a bilinear approach to generate different similarity functions with respect to different field pairs.Furthermore, the max-pooling method makes DeepEnFM feasible to capture both the supplementary and suppressing information among different attention heads.Our model is validated on the Criteo and Avazu datasets, and achieves state-of-art performance.", "target": ["DNN e FM con encoder migliorato con attention bilineare e max-pooling per CTR"]} +{"source": "For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence.In real-world scenarios, future states become increasingly uncertain and multi-modal, particularly on long time horizons.Dropout based Bayesian inference provides a computationally tractable, theoretically well grounded approach to learn different hypotheses/models to deal with uncertain futures and make predictions that correspond well to observations -- are well calibrated.However, it turns out that such approaches fall short to capture complex real-world scenes, even falling behind in accuracy when compared to the plain deterministic approaches.This is because the used log-likelihood estimate discourages diversity.In this work, we propose a novel Bayesian formulation for anticipating future scene states which leverages synthetic likelihoods that encourage the learning of diverse models to accurately capture the multi-modal nature of future scene states.We show that our approach achieves accurate state-of-the-art predictions and calibrated probabilities through extensive experiments for scene anticipation on Cityscapes dataset.Moreover, we show that our approach generalizes across diverse tasks such as digit generation and precipitation forecasting.", "target": ["L'inferenza bayesiana basata sul dropout è estesa per trattare la multi-modality ed è valutata su task di scene anticipation."]} +{"source": "Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision.The major focus so far has been on performance improvement, while there has been little effort in making cGAN more robust to noise.The regression (of the generator) might lead to arbitrarily large errors in the output, which makes cGAN unreliable for real-world applications.In this work, we introduce a novel conditional GAN model, called RoCGAN, which leverages structure in the target space of the model to address the issue.Our model augments the generator with an unsupervised pathway, which promotes the outputs of the generator to span the target manifold even in the presence of intense noise.We prove that RoCGAN share similar theoretical properties as GAN and experimentally verify that our model outperforms existing state-of-the-art cGAN architectures by a large margin in a variety of domains including images from natural scenes and faces.", "target": ["Introduciamo un nuovo tipo di GAN condizionale, che mira a sfruttare la struttura nello spazio target del generatore. Arricchiamo il generatore con un nuovo percorso unsupervised per imparare la struttura target."]} +{"source": "Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples.To solve the problem, some regularization adversarial training methods, constraining the output label or logit, have been studied.In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention.Instead of constraining a hard distribution (e.g., one-hot vectors or logit) in adversarial training, ATLPA uses Tolerant Logit which consists of confidence distribution on top-k classes and captures inter-class similarities at the image level.Specifically, in addition to minimizing the empirical loss, ATLPA encourages attention map for pairs of examples to be similar.When applied to clean examples and their adversarial counterparts, ATLPA improves accuracy on adversarial examples over adversarial training.We evaluate ATLPA with the state of the art algorithms, the experiment results show that our method outperforms these baselines with higher accuracy.Compared with previous work, our work is evaluated under highly challenging PGD attack: the maximum perturbation $\\epsilon$ is 64 and 128 with 10 to 200 attack iterations.", "target": ["In questo articolo, proponiamo un nuovo framework di training regolarizzato ATLPA, cioè Adversarial Tolerant Logit Pairing with Attention."]} +{"source": "A fundamental trait of intelligence is the ability to achieve goals in the face of novel circumstances.In this work, we address one such setting which requires solving a task with a novel set of actions.Empowering machines with this ability requires generalization in the way an agent perceives its available actions along with the way it uses these actions to solve tasks.Hence, we propose a framework to enable generalization over both these aspects: understanding an action’s functionality, and using actions to solve tasks through reinforcement learning.Specifically, an agent interprets an action’s behavior using unsupervised representation learning over a collection of data samples reflecting the diverse properties of that action.We employ a reinforcement learning architecture which works over these action representations, and propose regularization metrics essential for enabling generalization in a policy.We illustrate the generalizability of the representation learning method and policy, to enable zero-shot generalization to previously unseen actions on challenging sequential decision-making environments.Our results and videos can be found at sites.google.com/view/action-generalization/", "target": ["Affrontiamo il problema della generalizzazione del reinforcement learning a spazi d'azione non visti."]} +{"source": "Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals.The standard way of learning in such models is by estimating the conditional intensity function. However, parameterizing the intensity function usually incurs several trade-offs.We show how to overcome the limitations of intensity-based approaches by directly modeling the conditional distribution of inter-event times. We draw on the literature on normalizing flows to design models that are flexible and efficient.We additionally propose a simple mixture model that matches the flexibility of flow-based models, but also permits sampling and computing moments in closed form. The proposed models achieve state-of-the-art performance in standard prediction tasks and are suitable for novel applications, such as learning sequence embeddings and imputing missing data.", "target": ["Imparare in processi puntuali temporali modellando la densità condizionata, non l'intensità condizionata."]} +{"source": "We propose a novel yet simple neural network architecture for topic modelling.The method is based on training an autoencoder structure where the bottleneck represents the space of the topics distribution and the decoder outputs represent the space of the words distributions over the topics.We exploit an auxiliary decoder to prevent mode collapsing in our model. A key feature for an effective topic modelling method is having sparse topics and words distributions, where there is a trade-off between the sparsity level of topics and words.This feature is implemented in our model by L-2 regularization and the model hyperparameters take care of the trade-off. We show in our experiments that our model achieves competitive results compared to the state-of-the-art deep models for topic modelling, despite its simple architecture and training procedure.The “New York Times” and “20 Newsgroups” datasets are used in the experiments.", "target": ["Un deep model per topic modelling"]} +{"source": "Voice Conversion (VC) is a task of converting perceived speaker identity from a source speaker to a particular target speaker.Earlier approaches in the literature primarily find a mapping between the given source-target speaker-pairs.Developing mapping techniques for many-to-many VC using non-parallel data, including zero-shot learning remains less explored areas in VC.Most of the many-to-many VC architectures require training data from all the target speakers for whom we want to convert the voices.In this paper, we propose a novel style transfer architecture, which can also be extended to generate voices even for target speakers whose data were not used in the training (i.e., case of zero-shot learning).In particular, propose Adaptive Generative Adversarial Network (AdaGAN), new architectural training procedure help in learning normalized speaker-independent latent representation, which will be used to generate speech with different speaking styles in the context of VC.We compare our results with the state-of-the-art StarGAN-VC architecture.In particular, the AdaGAN achieves 31.73%, and 10.37% relative improvement compared to the StarGAN in MOS tests for speech quality and speaker similarity, respectively.The key strength of the proposed architectures is that it yields these results with less computational complexity.AdaGAN is 88.6% less complex than StarGAN-VC in terms of FLoating Operation Per Second (FLOPS), and 85.46% less complex in terms of trainable parameters.", "target": ["Un nuovo framework GAN basato sulla normalizzazione adattiva delle istanze per il VC non parallelo many-to-many e zero-shot."]} +{"source": "Self-attention-based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks.Self attention is able to model long-term dependencies, but it may suffer from the extraction of irrelevant information in the context.To tackle the problem, we propose a novel model called Sparse Transformer.Sparse Transformer is able to improve the concentration of attention on the global context through an explicit selection of the most relevant segments.Extensive experimental results on a series of natural language processing tasks, including neural machine translation, image captioning, and language modeling, all demonstrate the advantages of Sparse Transformer in model performance. Sparse Transformer reaches the state-of-the-art performances in the IWSLT 2015 English-to-Vietnamese translation and IWSLT 2014 German-to-English translation.In addition, we conduct qualitative analysis to account for Sparse Transformer's superior performance.", "target": ["Questo lavoro propone Sparse Transformer per migliorare la concentrazione dell'attention sul contesto globale attraverso una selezione esplicita dei segmenti più rilevanti per l'apprendimento sequenza per sequenza."]} +{"source": "Human observers can learn to recognize new categories of objects from a handful of examples, yet doing so with machine perception remains an open challenge.We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable, as suggested by recent perceptual evidence.We therefore revisit and improve Contrastive Predictive Coding, a recently-proposed unsupervised learning framework, and arrive at a representation which enables generalization from small amounts of labeled data.When provided with only 1% of ImageNet labels (i.e. 13 per class), this model retains a strong classification performance, 73% Top-5 accuracy, outperforming supervised networks by 28% (a 65% relative improvement) and state-of-the-art semi-supervised methods by 14%.We also find this representation to serve as a useful substrate for object detection on the PASCAL-VOC 2007 dataset, approaching the performance of representations trained with a fully annotated ImageNet dataset.", "target": ["Le rappresentazioni non supervised apprese con Contrastive Predictive Coding permettono una classificazione delle immagini efficiente dal punto di vista dei dati."]} +{"source": "We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels.We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently.Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer.Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights.We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms.Furthermore, we show that sparse momentum reliably reproduces dense performance levels while providing up to 5.61x faster training.In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network.", "target": ["La ridistribuzione e la crescita dei pesi in base alla grandezza del momentum permette il training di reti sparse da inizializzazioni casuali che possono raggiungere livelli di prestazioni delle controparti dense con il 5% fino al 50% dei pesi, accelerando il training fino a 5,6 volte."]} +{"source": "To provide principled ways of designing proper Deep Neural Network (DNN) models, it is essential to understand the loss surface of DNNs under realistic assumptions.We introduce interesting aspects for understanding the local minima and overall structure of the loss surface.The parameter domain of the loss surface can be decomposed into regions in which activation values (zero or one for rectified linear units) are consistent.We found that, in each region, the loss surface have properties similar to that of linear neural networks where every local minimum is a global minimum.This means that every differentiable local minimum is the global minimum of the corresponding region.We prove that for a neural network with one hidden layer using rectified linear units under realistic assumptions.There are poor regions that lead to poor local minima, and we explain why such regions exist even in the overparameterized DNNs.", "target": ["La superficie delle loss delle reti neurali è un'unione disgiunta di regioni dove ogni minimo locale è un minimo globale della regione corrispondente."]} +{"source": "Contextualized word representations, such as ELMo and BERT, were shown to perform well on a various of semantic and structural (syntactic) task.In this work, we tackle the task of unsupervised disentanglement between semantics and structure in neural language representations: we aim to learn a transformation of the contextualized vectors, that discards the lexical semantics, but keeps the structural information.To this end, we automatically generate groups of sentences which are structurally similar but semantically different, and use metric-learning approach to learn a transformation that emphasizes the structural component that is encoded in the vectors.We demonstrate that our transformation clusters vectors in space by structural properties, rather than by lexical semantics.Finally, we demonstrate the utility of our distilled representations by showing that they outperform the original contextualized representations in few-shot parsing setting.", "target": ["Distilliamo le rappresentazioni dei language model per la sintassi attraverso metric learning unsupervised"]} +{"source": "We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals.The proposed semi-parametric topological memory (SPTM) consists of a (non-parametric) graph with nodes corresponding to locations in the environment and a (parametric) deep network capable of retrieving nodes from the graph based on observations.The graph stores no metric information, only connectivity of locations corresponding to the nodes.We use SPTM as a planning module in a navigation system.Given only 5 minutes of footage of a previously unseen maze, an SPTM-based navigation agent can build a topological map of the environment and use it to confidently navigate towards goals.The average success rate of the SPTM agent in goal-directed navigation across test environments is higher than the best-performing baseline by a factor of three.", "target": ["Introduciamo una nuova architettura di memoria per la navigazione in ambienti mai visti prima, ispirata alla navigazione basata sui landmark negli animali."]} +{"source": "The available resolution in our visual world is extremely high, if not infinite.Existing CNNs can be applied in a fully convolutional way to images of arbitrary resolution, but as the size of the input increases, they can not capture contextual information.In addition, computational requirements scale linearly to the number of input pixels, and resources are allocated uniformly across the input, no matter how informative different image regions are.We attempt to address these problems by proposing a novel architecture that traverses an image pyramid in a top-down fashion, while it uses a hard attention mechanism to selectively process only the most informative image parts.We conduct experiments on MNIST and ImageNet datasets, and we show that our models can significantly outperform fully convolutional counterparts, when the resolution of the input is that big that the receptive field of the baselines can not adequately cover the objects of interest.Gains in performance come for less FLOPs, because of the selective processing that we follow.Furthermore, our attention mechanism makes our predictions more interpretable, and creates a trade-off between accuracy and complexity that can be tuned both during training and testing time.", "target": ["Proponiamo una nuova architettura che attraversa una piramide di immagini in modo top-down, visitando solo le regioni più informative lungo il percorso."]} +{"source": "Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters.We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters to optimal weights and biases.We show how to construct scalable best-response approximations for neural networks by modeling the best-response as a single network whose hidden units are gated conditionally on the regularizer.We justify this approximation by showing the exact best-response for a shallow linear network with L2-regularized Jacobian can be represented by a similar gating mechanism.We fit this model using a gradient-based hyperparameter optimization algorithm which alternates between approximating the best-response around the current hyperparameters and optimizing the hyperparameters using the approximate best-response function.Unlike other gradient-based approaches, we do not require differentiating the training loss with respect to the hyperparameters, allowing us to tune discrete hyperparameters, data augmentation hyperparameters, and dropout probabilities.Because the hyperparameters are adapted online, our approach discovers hyperparameter schedules that can outperform fixed hyperparameter values.Empirically, our approach outperforms competing hyperparameter optimization methods on large-scale deep learning problems.We call our networks, which update their own hyperparameters online during training, Self-Tuning Networks (STNs).", "target": ["Usiamo una hypernetwork per prevedere i pesi ottimali dati gli iperparametri, e performiamo joint training dell'intera architettura."]} +{"source": "Conditional Generative Adversarial Networks (cGANs) are finding increasingly widespread use in many application domains.Despite outstanding progress, quantitative evaluation of such models often involves multiple distinct metrics to assess different desirable properties, such as image quality, conditional consistency, and intra-conditioning diversity.In this setting, model benchmarking becomes a challenge, as each metric may indicate a different \"best\" model.In this paper, we propose the Frechet Joint Distance (FJD), which is defined as the Frechet distance between joint distributions of images and conditioning, allowing it to implicitly capture the aforementioned properties in a single metric.We conduct proof-of-concept experiments on a controllable synthetic dataset, which consistently highlight the benefits of FJD when compared to currently established metrics.Moreover, we use the newly introduced metric to compare existing cGAN-based models for a variety of conditioning modalities (e.g. class labels, object masks, bounding boxes, images, and text captions).We show that FJD can be used as a promising single metric for model benchmarking.", "target": ["Proponiamo una nuova metrica per valutare le GAN condizionali che cattura la qualità dell'immagine, la coerenza condizionale e la diversità intra-condizionata in un'unica misura."]} +{"source": "Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL.On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori.However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning.To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions.TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values.We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network.Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the tree.We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al., 2017) on multiple Atari games.Furthermore, we present ablation studies that demonstrate the effect of different auxiliary losses on learning transition models.", "target": ["Presentiamo TreeQN e ATreeC, nuove architetture per il deep reinforcement learning in domini ad azione discreta che integrano la pianificazione ad albero on-line differenziabile nella funzione o policy action-value."]} +{"source": "Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample.Modeling the combinatorial label interactions in MLC has been a long-haul challenge.Recurrent neural network (RNN) based encoder-decoder models have shown state-of-the-art performance for solving MLC.However, the sequential nature of modeling label dependencies through an RNN limits its ability in parallel computation, predicting dense labels, and providing interpretable results.In this paper, we propose Message Passing Encoder-Decoder (MPED) Networks, aiming to provide fast, accurate, and interpretable MLC.MPED networks model the joint prediction of labels by replacing all RNNs in the encoder-decoder architecture with message passing mechanisms and dispense with autoregressive inference entirely. The proposed models are simple, fast, accurate, interpretable, and structure-agnostic (can be used on known or unknown structured data).Experiments on seven real-world MLC datasets show the proposed models outperform autoregressive RNN models across five different metrics with a significant speedup during training and testing time.", "target": ["Proponiamo reti di message passing encoder-decoder per un modo veloce e accurato di modellare le dipendenze delle label per la classificazione multilabel."]} +{"source": "Recent few-shot learning algorithms have enabled models to quickly adapt to new tasks based on only a few training samples.Previous few-shot learning works have mainly focused on classification and reinforcement learning.In this paper, we propose a few-shot meta-learning system that focuses exclusively on regression tasks.Our model is based on the idea that the degree of freedom of the unknown function can be significantly reduced if it is represented as a linear combination of a set of sparsifying basis functions.This enables a few labeled samples to approximate the function.We design a Basis Function Learner network to encode basis functions for a task distribution, and a Weights Generator network to generate the weight vector for a novel task.We show that our model outperforms the current state of the art meta-learning methods in various regression tasks.", "target": ["Proponiamo un metodo per fare la regressione few-shot imparando un insieme di funzioni di base per rappresentare la distribuzione della funzione."]} +{"source": "Large-scale pre-trained language model, such as BERT, has recently achieved great success in a wide range of language understanding tasks.However, it remains an open question how to utilize BERT for text generation tasks.In this paper, we present a novel approach to addressing this challenge in a generic sequence-to-sequence (Seq2Seq) setting.We first propose a new task, Conditional Masked Language Modeling (C-MLM), to enable fine-tuning of BERT on target text-generation dataset.The fine-tuned BERT (i.e., teacher) is then exploited as extra supervision to improve conventional Seq2Seq models (i.e., student) for text generation.By leveraging BERT's idiosyncratic bidirectional nature, distilling the knowledge learned from BERT can encourage auto-regressive Seq2Seq models to plan ahead, imposing global sequence-level supervision for coherent text generation.Experiments show that the proposed approach significantly outperforms strong baselines of Transformer on multiple text generation tasks, including machine translation (MT) and text summarization.Our proposed model also achieves new state-of-the-art results on the IWSLT German-English and English-Vietnamese MT datasets.", "target": ["Proponiamo un modo indipendente dal modello per sfruttare BERT per la generazione di testo e ottenere miglioramenti rispetto a Transformer su 2 task in 4 dataset."]} +{"source": "Humans have the remarkable ability to correctly classify images despite possible degradation.Many studies have suggested that this hallmark of human vision results from the interaction between feedforward signals from bottom-up pathways of the visual cortex and feedback signals provided by top-down pathways.Motivated by such interaction, we propose a new neuro-inspired model, namely Convolutional Neural Networks with Feedback (CNN-F).CNN-F extends CNN with a feedback generative network, combining bottom-up and top-down inference to perform approximate loopy belief propagation. We show that CNN-F's iterative inference allows for disentanglement of latent variables across layers.We validate the advantages of CNN-F over the baseline CNN.Our experimental results suggest that the CNN-F is more robust to image degradation such as pixel noise, occlusion, and blur. Furthermore, we show that the CNN-F is capable of restoring original images from the degraded ones with high reconstruction accuracy while introducing negligible artifacts.", "target": ["CNN-F estende CNN con una rete generativa di feedback per una visione robusta."]} +{"source": "We develop new approximation and statistical learning theories of convolutional neural networks (CNNs) via the ResNet-type structure where the channel size, filter size, and width are fixed.It is shown that a ResNet-type CNN is a universal approximator and its expression ability is no worse than fully-connected neural networks (FNNs) with a \\textit{block-sparse} structure even if the size of each layer in the CNN is fixed.Our result is general in the sense that we can automatically translate any approximation rate achieved by block-sparse FNNs into that by CNNs.Thanks to the general theory, it is shown that learning on CNNs satisfies optimality in approximation and estimation of several important function classes.As applications, we consider two types of function classes to be estimated: the Barron class and H\\\"older class.We prove the clipped empirical risk minimization (ERM) estimator can achieve the same rate as FNNs even the channel size, filter size, and width of CNNs are constant with respect to the sample size.This is minimax optimal (up to logarithmic factors) for the H\\\"older class.Our proof is based on sophisticated evaluations of the covering number of CNNs and the non-trivial parameter rescaling technique to control the Lipschitz constant of CNNs to be constructed.", "target": ["Si dimostra che le CNN di tipo ResNet sono un approssimatore universale e la sua capacità di espressione non è peggiore delle reti neurali completamente connesse (FNN) con una struttura block-sparse anche se la dimensione di ogni layer nella CNN è fissa."]} +{"source": "Few shot image classification aims at learning a classifier from limited labeled data.Generating the classification weights has been applied in many meta-learning approaches for few shot image classification due to its simplicity and effectiveness.However, we argue that it is difficult to generate the exact and universal classification weights for all the diverse query samples from very few training samples.In this work, we introduce Attentive Weights Generation for few shot learning via Information Maximization (AWGIM), which addresses current issues by two novel contributions.i) AWGIM generates different classification weights for different query samples by letting each of query samples attends to the whole support set.ii) To guarantee the generated weights adaptive to different query sample, we re-formulate the problem to maximize the lower bound of mutual information between generated weights and query as well as support data.As far as we can see, this is the first attempt to unify information maximization into few shot learning.Both two contributions are proved to be effective in the extensive experiments and we show that AWGIM is able to achieve state-of-the-art performance on benchmark datasets.", "target": ["Un nuovo metodo di few shot learning per generare pesi di classificazione specifici per la query attraverso la massimizzazione dell'informazione."]} +{"source": "Conversational question answering (CQA) is a novel QA task that requires the understanding of dialogue context.Different from traditional single-turn machine reading comprehension (MRC), CQA is a comprehensive task comprised of passage reading, coreference resolution, and contextual understanding.In this paper, we propose an innovative contextualized attention-based deep neural network, SDNet, to fuse context into traditional MRC models.Our model leverages both inter-attention and self-attention to comprehend the conversation and passage.Furthermore, we demonstrate a novel method to integrate the BERT contextual model as a sub-module in our network.Empirical results show the effectiveness of SDNet.On the CoQA leaderboard, it outperforms the previous best model's F1 score by 1.6%.Our ensemble model further improves the F1 score by 2.7%.", "target": ["Un metodo neurale per la question answering conversazionale con meccanismo di attention e un nuovo uso di BERT come embedder contestuale"]} +{"source": "Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality.However, Wasserstein GANs require the critic to be 1-Lipschitz, which is often enforced implicitly by penalizing the norm of its gradient, or by globally restricting its Lipschitz constant via weight normalization techniques.Training with a regularization term penalizing the violation of the Lipschitz constraint explicitly, instead of through the norm of the gradient, was found to be practically infeasible in most situations.Inspired by Virtual Adversarial Training, we propose a method called Adversarial Lipschitz Regularization, and show that using an explicit Lipschitz penalty is indeed viable and leads to competitive performance when applied to Wasserstein GANs, highlighting an important connection between Lipschitz regularization and adversarial training.", "target": ["alternativa alla gradient penalty"]} +{"source": "Multi-task learning promises to use less data, parameters, and time than training separate single-task models.But realizing these benefits in practice is challenging.In particular, it is difficult to define a suitable architecture that has enough capacity to support many tasks while not requiring excessive compute for each individual task.There are difficult trade-offs when deciding how to allocate parameters and layers across a large set of tasks.To address this, we propose a method for automatically searching over multi-task architectures that accounts for resource constraints.We define a parameterization of feature sharing strategies for effective coverage and sampling of architectures.We also present a method for quick evaluation of such architectures with feature distillation.Together these contributions allow us to quickly optimize for parameter-efficient multi-task models.We benchmark on Visual Decathlon, demonstrating that we can automatically search for and identify architectures that effectively make trade-offs between task resource requirements while maintaining a high level of final performance.", "target": ["ricerca automatica di architetture multi-task che riducono l'uso di feature per-task"]} +{"source": "As distributed approaches to natural language semantics have developed and diversified, embedders for linguistic units larger than words (e.g., sentences) have come to play an increasingly important role. To date, such embedders have been evaluated using benchmark tasks (e.g., GLUE) and linguistic probes. We propose a comparative approach, nearest neighbor overlap (N2O), that quantifies similarity between embedders in a task-agnostic manner. N2O requires only a collection of examples and is simple to understand: two embedders are more similar if, for the same set of inputs, there is greater overlap between the inputs' nearest neighbors. We use N2O to compare 21 sentence embedders and show the effects of different design choices and architectures.", "target": ["Proponiamo la nearest neighbor overlap, una procedura che quantifica la somiglianza tra gli embedder in modo indipendente dal task, e la usiamo per confrontare 21 sentence embedder."]} +{"source": "Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem.Variational Autoencoders (VAE) on the other hand explicitly maximize a reconstruction-based data log-likelihood forcing it to cover all modes, but suffer from poorer sample quality.Recent works have proposed hybrid VAE-GAN frameworks which integrate a GAN-based synthetic likelihood to the VAE objective to address both the mode collapse and sample quality issues, with limited success.This is because the VAE objective forces a trade-off between the data log-likelihood and divergence to the latent prior.The synthetic likelihood ratio term also shows instability during training.We propose a novel objective with a ``\"Best-of-Many-Samples\" reconstruction cost and a stable direct estimate of the synthetic likelihood.This enables our hybrid VAE-GAN framework to achieve high data log-likelihood and low divergence to the latent prior at the same time and shows significant improvement over both hybrid VAE-GANS and plain GANs in mode coverage and quality.", "target": ["Proponiamo un nuovo obiettivo per il training di VAE-GAN ibride che portano a un miglioramento significativo della mode coverage e quality."]} +{"source": "In order to mimic the human ability of continual acquisition and transfer of knowledge across various tasks, a learning system needs the capability for life-long learning, effectively utilizing the previously acquired skills.As such, the key challenge is to transfer and generalize the knowledge learned from one task to other tasks, avoiding interference from previous knowledge and improving the overall performance.In this paper, within the continual learning paradigm, we introduce a method that effectively forgets the less useful data samples continuously across different tasks.The method uses statistical leverage score information to measure the importance of the data samples in every task and adopts frequent directions approach to enable a life-long learning property.This effectively maintains a constant training size across all tasks.We first provide some mathematical intuition for the method and then demonstrate its effectiveness with experiments on variants of MNIST and CIFAR100 datasets.", "target": ["Un nuovo metodo usa l'informazione statistica del leverage score per misurare l'importanza dei sample di dati in ogni task e adotta un approccio di direzioni frequenti per permettere una proprietà di lifelong learning."]} +{"source": "Convolutional neural networks (CNNs) are inherently equivariant to translation.Efforts to embed other forms of equivariance have concentrated solely on rotation.We expand the notion of equivariance in CNNs through the Polar Transformer Network (PTN).PTN combines ideas from the Spatial Transformer Network (STN) and canonical coordinate representations.The result is a network invariant to translation and equivariant to both rotation and scale.PTN is trained end-to-end and composed of three distinct stages: a polar origin predictor, the newly introduced polar transformer module and a classifier.PTN achieves state-of-the-art on rotated MNIST and the newly introduced SIM2MNIST dataset, an MNIST variation obtained by adding clutter and perturbing digits with translation, rotation and scaling.The ideas of PTN are extensible to 3D which we demonstrate through the Cylindrical Transformer Network.", "target": ["Impariamo mappe di feature invarianti alla traslazione, ed equivarianti alla rotazione e alla scala."]} +{"source": "Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task.Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks.Here, we reformulate the model-agnostic meta-learning algorithm (MAML) of Finn et al. (2017) as a method for probabilistic inference in a hierarchical Bayesian model.In contrast to prior methods for meta-learning via hierarchical Bayes, MAML is naturally applicable to complex function approximators through its use of a scalable gradient descent procedure for posterior inference.Furthermore, the identification of MAML as hierarchical Bayes provides a way to understand the algorithm’s operation as a meta-learning procedure, as well as an opportunity to make use of computational strategies for efficient inference.We use this opportunity to propose an improvement to the MAML algorithm that makes use of techniques from approximate inference and curvature estimation.", "target": ["Uno specifico algoritmo di meta-learning basato sul gradiente, MAML, è equivalente a una procedura di inferenza in un modello gerarchico bayesiano. Usiamo questa connessione per migliorare MAML attraverso metodi di inferenza approssimativa e stima della curvatura."]} +{"source": "This work provides an automatic machine learning (AutoML) modelling architecture called Autostacker.Autostacker improves the prediction accuracy of machine learning baselines by utilizing an innovative hierarchical stacking architecture and an efficient parameter search algorithm.Neither prior domain knowledge about the data nor feature preprocessing is needed.We significantly reduce the time of AutoML with a naturally inspired algorithm - Parallel Hill Climbing (PHC).By parallelizing PHC, Autostacker can provide candidate pipelines with sufficient prediction accuracy within a short amount of time.These pipelines can be used as is or as a starting point for human experts to build on.By focusing on the modelling process, Autostacker breaks the tradition of following fixed order pipelines by exploring not only single model pipeline but also innovative combinations and structures.As we will show in the experiment section, Autostacker achieves significantly better performance both in terms of test accuracy and time cost comparing with human initial trials and recent popular AutoML system.", "target": ["Automatizzare il sistema di machine learning con un algoritmo di ricerca efficiente e una struttura innovativa per fornire migliori modelli baseline."]} +{"source": "Surrogate models can be used to accelerate approximate Bayesian computation (ABC).In one such framework the discrepancy between simulated and observed data is modelled with a Gaussian process.So far principled strategies have been proposed only for sequential selection of the simulation locations.To address this limitation, we develop Bayesian optimal design strategies to parallellise the expensive simulations.We also touch the problem of quantifying the uncertainty of the ABC posterior due to the limited budget of simulations.", "target": ["Proponiamo strategie di progettazione sperimentale batch bayesiana basate su principi teorici e un metodo per la quantificazione dell'incertezza dei sommari posteriori in un framework di calcolo bayesiano approssimato basato su un surrogato di processo gaussiano."]} +{"source": "Randomly initialized first-order optimization algorithms are the method of choice for solving many high-dimensional nonconvex problems in machine learning, yet general theoretical guarantees cannot rule out convergence to critical points of poor objective value.For some highly structured nonconvex problems however, the success of gradient descent can be understood by studying the geometry of the objective.We study one such problem -- complete orthogonal dictionary learning, and provide converge guarantees for randomly initialized gradient descent to the neighborhood of a global optimum.The resulting rates scale as low order polynomials in the dimension even though the objective possesses an exponential number of saddle points.This efficient convergence can be viewed as a consequence of negative curvature normal to the stable manifolds associated with saddle points, and we provide evidence that this feature is shared by other nonconvex problems of importance as well.", "target": ["Forniamo un tasso di convergenza efficiente per la gradient descent sull'obiettivo del complete orthogonal dictionary learning basato su un'analisi geometrica."]} +{"source": "Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age.Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century.In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning.We study a family of sequential codes parametrized by recurrent neural network (RNN) architectures.We show that cre- atively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms).We show strong gen- eralizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.", "target": ["Mostriamo che architetture RNN progettate e addestrate in modo creativo possono decodificare codici sequenziali ben noti e raggiungere prestazioni quasi ottimali."]} +{"source": "Adam is shown not being able to converge to the optimal solution in certain cases.Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, but their efficiency turns out to be unsatisfactory in practice.In this paper, we provide a new insight into the non-convergence issue of Adam as well as other adaptive learning rate methods.We argue that there exists an inappropriate correlation between gradient $g_t$ and the second moment term $v_t$ in Adam ($t$ is the timestep), which results in that a large gradient is likely to have small step size while a small gradient may have a large step size.We demonstrate that such unbalanced step sizes are the fundamental cause of non-convergence of Adam, and we further prove that decorrelating $v_t$ and $g_t$ will lead to unbiased step size for each gradient, thus solving the non-convergence problem of Adam.Finally, we propose AdaShift, a novel adaptive learning rate method that decorrelates $v_t$ and $g_t$ by temporal shifting, i.e., using temporally shifted gradient $g_{t-n}$ to calculate $v_t$.The experiment results demonstrate that AdaShift is able to address the non-convergence issue of Adam, while still maintaining a competitive performance with Adam in terms of both training speed and generalization.", "target": ["Analizziamo e risolviamo il problema della non convergenza di Adam."]} +{"source": "Most domain adaptation methods consider the problem of transferring knowledge to the target domain from a single source dataset.However, in practical applications, we typically have access to multiple sources.In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks.Our method is inspired by the observation that the appearance of a given image depends on three factors: the domain, the style (characterized in terms of low-level features variations) and the content.For this reason we propose to project the image features onto a space where only the dependence from the content is kept, and then re-project this invariant representation onto the pixel space using the target domain and style.In this way, new labeled images can be generated which are used to train a final target classifier.We test our approach using common MSDA benchmarks, showing that it outperforms state-of-the-art methods.", "target": ["In questo articolo proponiamo un metodo generativo per multisource domain adaptation basato sulla decomposizione dei fattori di contenuto, stile e dominio."]} +{"source": "Inferring the most likely configuration for a subset of variables of a joint distribution given the remaining ones – which we refer to as co-generation – is an important challenge that is computationally demanding for all but the simplest settings.This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction.In contrast, almost nothing is known about this task when considering recently proposed techniques for modeling high-dimensional distributions, particularly generative adversarial nets (GANs).Therefore, in this paper, we study the occurring challenges for co-generation with GANs.To address those challenges we develop an annealed importance sampling (AIS) based Hamiltonian Monte Carlo (HMC) co-generation algorithm.The presented approach significantly outperforms classical gradient-based methods on synthetic data and on CelebA.", "target": ["Utilizzo dell'annealed importance sampling sul problema della co-generazione."]} +{"source": "Implicit probabilistic models are models defined naturally in terms of a sampling procedure and often induces a likelihood function that cannot be expressed explicitly.We develop a simple method for estimating parameters in implicit models that does not require knowledge of the form of the likelihood function or any derived quantities, but can be shown to be equivalent to maximizing likelihood under some conditions.Our result holds in the non-asymptotic parametric setting, where both the capacity of the model and the number of data examples are finite.We also demonstrate encouraging experimental results.", "target": ["Sviluppiamo un nuovo metodo di stima dei parametri senza likelihood che è equivalente alla massima verosimiglianza sotto alcune condizioni"]} +{"source": "While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on estimating a reward function that best explains an expert agent’s policy or demonstrated behavior on a control task, it is often the case that such behavior is more succinctly represented by a simple reward combined with a set of hard constraints.In this setting, the agent is attempting to maximize cumulative rewards subject to these given constraints on their behavior.We reformulate the problem of IRL on Markov Decision Processes (MDPs) such that, given a nominal model of the environment and a nominal reward function, we seek to estimate state, action, and feature constraints in the environment that motivate an agent’s behavior.Our approach is based on the Maximum Entropy IRL framework, which allows us to reason about the likelihood of an expert agent’s demonstrations given our knowledge of an MDP.Using our method, we can infer which constraints can be added to the MDP to most increase the likelihood of observing these demonstrations.We present an algorithm which iteratively infers the Maximum Likelihood Constraint to best explain observed behavior, and we evaluate its efficacy using both simulated behavior and recorded data of humans navigating around an obstacle.", "target": ["Il nostro metodo deduce i vincoli sull'esecuzione dei task sfruttando il principio della massima entropia per quantificare come le dimostrazioni differiscono dal comportamento atteso, senza vincoli."]} +{"source": "The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.In this work, we discuss the required elements of a robotic system that can continually and autonomously improve with data collected in the real world, and propose a particular instantiation of such a system.Subsequently, we investigate a number of challenges of learning without instrumentation -- including the lack of episodic resets, state estimation, and hand-engineered rewards -- and propose simple, scalable solutions to these challenges.We demonstrate the efficacy of our proposed system on dexterous robotic manipulation tasks in simulation and the real world, and also provide an insightful analysis and ablation study of the challenges associated with this learning paradigm.", "target": ["Sistema per imparare task robotici nel mondo reale con reinforcement learning senza strumentazione"]} +{"source": "We study the problem of cross-lingual voice conversion in non-parallel speech corpora and one-shot learning setting.Most prior work require either parallel speech corpora or enough amount of training data from a target speaker.However, we convert an arbitrary sentences of an arbitrary source speaker to target speaker's given only one target speaker training utterance.To achieve this, we formulate the problem as learning disentangled speaker-specific and context-specific representations and follow the idea of [1] which uses Factorized Hierarchical Variational Autoencoder (FHVAE).After training FHVAE on multi-speaker training data, given arbitrary source and target speakers' utterance, we estimate those latent representations and then reconstruct the desired utterance of converted voice to that of target speaker.We use multi-language speech corpus to learn a universal model that works for all of the languages.We investigate the use of a one-hot language embedding to condition the model on the language of the utterance being queried and show the effectiveness of the approach.We conduct voice conversion experiments with varying size of training utterances and it was able to achieve reasonable performance with even just one training utterance.We also investigate the effect of using or not using the language conditioning.Furthermore, we visualize the embeddings of the different languages and sexes.Finally, in the subjective tests, for one language and cross-lingual voice conversion, our approach achieved moderately better or comparable results compared to the baseline in speech quality and similarity.", "target": ["Usiamo un Variational Autoencoder per separare lo stile e il contenuto, e otteniamo la conversione della voce modificando l'embedding e la decodifica dello stile. Indaghiamo utilizzando un corpus vocale multilingue e analizziamo i suoi effetti."]} +{"source": "We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of scores for each map.Standard CNN architectures are modified through the incorporation of this module, and trained under the constraint that a convex combination of the intermediate 2D feature vectors, as parametrised by the score matrices, must alone be used for classification.Incentivised to amplify the relevant and suppress the irrelevant or misleading, the scores thus assume the role of attention values.Our experimental observations provide clear evidence to this effect: the learned attention maps neatly highlight the regions of interest while suppressing background clutter.Consequently, the proposed function is able to bootstrap standard CNN architectures for the task of image classification, demonstrating superior generalisation over 6 unseen benchmark datasets.When binarised, our attention maps outperform other CNN-based attention maps, traditional saliency maps, and top object proposals for weakly supervised segmentation as demonstrated on the Object Discovery dataset.We also demonstrate improved robustness against the fast gradient sign method of adversarial attack.", "target": ["L'articolo propone un metodo per costringere le CNN a sfruttare l'attention spaziale nell'apprendimento di rappresentazioni più centrate sull'oggetto che si comportano meglio sotto vari aspetti."]} +{"source": "Recurrent neural network(RNN) is an effective neural network in solving very complex supervised and unsupervised tasks.There has been a significant improvement in RNN field such as natural language processing, speech processing, computer vision and other multiple domains.This paper deals with RNN application on different use cases like Incident Detection , Fraud Detection , and Android Malware Classification.The best performing neural network architecture is chosenby conducting different chain of experiments for different network parameters and structures.The network is run up to 1000 epochs with learning rate set in the range of 0.01 to 0.5.Obviously, RNN performed very well when compared to classical machine learning algorithms.This is mainly possible because RNNs implicitly extracts the underlying features and also identifies the characteristics of the data.This lead to better accuracy.", "target": ["Reti neurali ricorrenti per i casi d'uso della Cybersecurity"]} +{"source": "Anatomical studies demonstrate that brain reformats input information to generate reliable responses for performing computations.However, it remains unclear how neural circuits encode complex spatio-temporal patterns.We show that neural dynamics are strongly influenced by the phase alignment between the input and the spontaneous chaotic activity.Input alignment along the dominant chaotic projections causes the chaotic trajectories to become stable channels (or attractors), hence, improving the computational capability of a recurrent network.Using mean field analysis, we derive the impact of input alignment on the overall stability of attractors formed.Our results indicate that input alignment determines the extent of intrinsic noise suppression and hence, alters the attractor state stability, thereby controlling the network's inference ability.", "target": ["Strutturazione degli input lungo il caos per la stabilità"]} +{"source": "Generative adversarial networks are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions.GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t.the generative parameters, and thus do not work for discrete data.We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.The importance weights have a strong connection to the decision boundary of the discriminator, and we call our method boundary-seeking GANs (BGANs).We demonstrate the effectiveness of the proposed algorithm with discrete image and character-based natural language generation. In addition, the boundary-seeking objective extends to continuous data, which can be used to improve stability of training, and we demonstrate this on Celeba, Large-scale Scene Understanding (LSUN) bedrooms, and Imagenet without conditioning.", "target": ["Ci occupiamo del training delle GAN con dati discreti formulando un gradiente di policy che generalizza attraverso le f-divergenze"]} +{"source": "Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates.The high variance problem is particularly exasperated in problems with long horizons or high-dimensional action spaces.To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP.We demonstrate and quantify the benefit of the action-dependent baseline through both theoretical analysis as well as numerical results, including an analysis of the suboptimality of the optimal state-dependent baseline.The result is a computationally efficient policy gradient algorithm, which scales to high-dimensional control problems, as demonstrated by a synthetic 2000-dimensional target matching task.Our experimental results indicate that action-dependent baselines allow for faster learning on standard reinforcement learning benchmarks and high-dimensional hand manipulation and synthetic tasks.Finally, we show that the general idea of including additional information in baselines for improved variance reduction can be extended to partially observed and multi-agent tasks.", "target": ["Le baseline dipendenti dall'azione possono essere prive di bias e produrre una maggiore riduzione della varianza rispetto alle baseline dipendenti solo dallo stato per i metodi policy gradient."]} +{"source": "The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches.The problem is further exacerbated when supervised learning is applied to a number of correlated tasks simultaneously since the amount of labels required scales with the number of tasks.To mitigate this concern, we propose an active multitask learning algorithm that achieves knowledge transfer between tasks.The approach forms a so-called committee for each task that jointly makes decisions and directly shares data across similar tasks.Our approach reduces the number of queries needed during training while maintaining high accuracy on test data.Empirical results on benchmark datasets show significant improvements on both accuracy and number of query requests.", "target": ["Proponiamo un algoritmo attivo di multitask learning che realizza il transfer di conoscenza tra i task."]} +{"source": "Detection of photo manipulation relies on subtle statistical traces, notoriously removed by aggressive lossy compression employed online.We demonstrate that end-to-end modeling of complex photo dissemination channels allows for codec optimization with explicit provenance objectives.We design a lightweight trainable lossy image codec, that delivers competitive rate-distortion performance, on par with best hand-engineered alternatives, but has lower computational footprint on modern GPU-enabled platforms.Our results show that significant improvements in manipulation detection accuracy are possible at fractional costs in bandwidth/storage.Our codec improved the accuracy from 37% to 86% even at very low bit-rates, well below the practicality of JPEG (QF 20).", "target": ["Impariamo un efficiente codec di immagini lossy che può essere ottimizzato per facilitare il rilevamento affidabile della manipolazione delle foto a un costo frazionato in termini di payload/quality e anche a bitrate bassi."]} +{"source": "Recurrent Neural Networks have long been the dominating choice for sequence modeling.However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure.Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently.Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks.Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts.In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks.The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings.We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks.", "target": ["Questo articolo propone un efficace modello generico di sequenza che sfrutta i punti di forza delle RNN e della multi-head attention."]} +{"source": "Many tasks in natural language processing and related domains require high precision output that obeys dataset-specific constraints.This level of fine-grained control can be difficult to obtain in large-scale neural network models.In this work, we propose a structured latent-variable approach that adds discrete control states within a standard autoregressive neural paradigm.Under this formulation, we can include a range of rich, posterior constraints to enforce task-specific knowledge that is effectively trained into the neural model.This approach allows us to provide arbitrary grounding of internal model decisions, without sacrificing any representational power of neural models.Experiments consider applications of this approach for text generation and part-of-speech induction.For natural language generation, we find that this method improves over standard benchmarks, while also providing fine-grained control.", "target": ["Un approccio strutturato a variabili latenti che aggiunge stati di controllo discreti all'interno di un paradigma neurale autoregressivo standard per fornire un fondamento arbitrario delle decisioni interne del modello, senza sacrificare alcun potere di rappresentazione dei modelli neurali."]} +{"source": "Suppose a deep classification model is trained with samples that need to be kept private for privacy or confidentiality reasons.In this setting, can an adversary obtain the private samples if the classification model is given to the adversary?We call this reverse engineering against the classification model the Classifier-to-Generator (C2G) Attack.This situation arises when the classification model is embedded into mobile devices for offline prediction (e.g., object recognition for the automatic driving car and face recognition for mobile phone authentication).For C2G attack, we introduce a novel GAN, PreImageGAN.In PreImageGAN, the generator is designed to estimate the the sample distribution conditioned by the preimage of classification model $f$, $P(X|f(X)=y)$, where $X$ is the random variable on the sample space and $y$ is the probability vector representing the target label arbitrary specified by the adversary.In experiments, we demonstrate PreImageGAN works successfully with hand-written character recognition and face recognition.In character recognition, we show that, given a recognition model of hand-written digits, PreImageGAN allows the adversary to extract alphabet letter images without knowing that the model is built for alphabet letter images.In face recognition, we show that, when an adversary obtains a face recognition model for a set of individuals, PreImageGAN allows the adversary to extract face images of specific individuals contained in the set, even when the adversary has no knowledge of the face of the individuals.", "target": ["Stima della distribuzione dei dati di training dal classificatore addestrato usando GAN."]} +{"source": "The goal of standard compressive sensing is to estimate an unknown vector from linear measurements under the assumption of sparsity in some basis.Recently, it has been shown that significantly fewer measurements may be required if the sparsity assumption is replaced by the assumption that the unknown vector lies near the range of a suitably-chosen generative model. In particular, in (Bora {\\em et al.}, 2017) it was shown that roughly $O(k\\log L)$ random Gaussian measurements suffice for accurate recovery when the $k$-input generative model is bounded and $L$-Lipschitz, and that $O(kd \\log w)$ measurements suffice for $k$-input ReLU networks with depth $d$ and width $w$. In this paper, we establish corresponding algorithm-independent lower bounds on the sample complexity using tools from minimax statistical analysis. In accordance with the above upper bounds, our results are summarized as follows: (i) We construct an $L$-Lipschitz generative model capable of generating group-sparse signals, and show that the resulting necessary number of measurements is $\\Omega(k \\log L)$;(ii) Using similar ideas, we construct two-layer ReLU networks of high width requiring $\\Omega(k \\log w)$ measurements, as well as lower-width deep ReLU networks requiring $\\Omega(k d)$ measurements. As a result, we establish that the scaling laws derived in (Bora {\\em et al.}, 2017) are optimal or near-optimal in the absence of further assumptions.", "target": ["Stabiliamo che le leggi di scala derivate in (Bora et al., 2017) sono ottimali o quasi ottimali in assenza di ulteriori ipotesi."]} +{"source": "Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents.Here we explore whether modern deep reinforcement learning can be used to train agents to perform causal reasoning.We adopt a meta-learning approach, where the agent learns a policy for conducting experiments via causal interventions, in order to support a subsequent task which rewards making accurate causal inferences.We also found the agent could make sophisticated counterfactual predictions, as well as learn to draw causal inferences from purely observational data.Though powerful formalisms for causal reasoning have been developed, applying them in real-world domains can be difficult because fitting to large amounts of high dimensional data often requires making idealized assumptions.Our results suggest that causal reasoning in complex settings may benefit from powerful learning-based approaches.More generally, this work may offer new strategies for structured exploration in reinforcement learning, by providing agents with the ability to perform—and interpret—experiments.", "target": ["Trovare un algoritmo di apprendimento tramite meta-learning capace di ragionamento causale"]} +{"source": "Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews.To employ rule based sentiment classification, we require sentiment lexicons.However, manual construction of sentiment lexicon is time consuming and costly for resource-limited languages.To bypass manual development time and costs, we tried to build Amharic Sentiment Lexicons relying on corpus based approach.The intention of this approach is to handle sentiment terms specific to Amharic language from Amharic Corpus.Small set of seed terms are manually prepared from three parts of speech such as noun, adjective and verb.We developed algorithms for constructing Amharic sentiment lexicons automatically from Amharic news corpus.Corpus based approach is proposed relying on the word co-occurrence distributional embedding including frequency based embedding (i.e. Positive Point-wise Mutual Information PPMI).First we build word-context unigram frequency count matrix and transform it to point-wise mutual Information matrix.Using this matrix, we computed the cosine distance of mean vector of seed lists and each word in the corpus vocabulary.Based on the threshold value, the top closest words to the mean vector of seed list are added to the lexicon.Then the mean vector of the new sentiment seed list is updated and process is repeated until we get sufficient terms in the lexicon.Using PPMI with threshold value of 100 and 200, we got corpus based Amharic Sentiment lexicons of size 1811 and 3794 respectively by expanding 519 seeds.Finally, the lexicon generated in corpus based approach is evaluated.", "target": ["L'algoritmo basato sul corpus è sviluppato per generare l'Amharic Sentiment lexicon basandosi sul corpus"]} +{"source": "Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL).In the tabular case, all provably efficient model-free algorithms rely on it.However, model-free deep RL algorithms do not use optimistic initialisation despite taking inspiration from these provably efficient tabular algorithms.In particular, in scenarios with only positive rewards, Q-values are initialised at their lowest possible values due to commonly used network initialisation schemes, a pessimistic initialisation.Merely initialising the network to output optimistic Q-values is not enough, since we cannot ensure that they remain optimistic for novel state-action pairs, which is crucial for exploration.We propose a simple count-based augmentation to pessimistically initialised Q-values that separates the source of optimism from the neural network.We show that this scheme is provably efficient in the tabular setting and extend it to the deep RL setting.Our algorithm, Optimistic Pessimistically Initialised Q-Learning (OPIQ), augments the Q-value estimates of a DQN-based agent with count-derived bonuses to ensure optimism during both action selection and bootstrapping.We show that OPIQ outperforms non-optimistic DQN variants that utilise a pseudocount-based intrinsic motivation in hard exploration tasks, and that it predicts optimistic estimates for novel state-action pairs.", "target": ["Noi aumentiamo le stime del valore Q con un bonus basato sul conteggio che assicura l'ottimismo durante la selezione delle azioni e il bootstrapping, anche se le stime del valore Q sono pessimistiche."]} +{"source": "Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks.However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning.Both of these challenges severely limit the applicability of such methods to complex, real-world domains.In this paper, we propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework.In this framework, the actor aims to maximize expected reward while also maximizing entropy - that is, succeed at the task while acting as randomly as possible.Prior deep RL methods based on this framework have been formulated as either off-policy Q-learning, or on-policy policy gradient methods.By combining off-policy updates with a stable stochastic actor-critic formulation, our method achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off-policy methods.Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving very similar performance across different random seeds.", "target": ["Proponiamo soft actor-critic, un algoritmo di RL profondo off-policy actor-critic basato sul framework di reinforcement learning a massima entropia."]} +{"source": "In many settings, it is desirable to learn decision-making and control policies through learning or from expert demonstrations.The most common approaches under this framework are Behaviour Cloning (BC), and Inverse Reinforcement Learning (IRL).Recent methods for IRL have demonstrated the capacity to learn effective policies with access to a very limited set of demonstrations, a scenario in which BC methods often fail.Unfortunately, directly comparing the algorithms for these methods does not provide adequate intuition for understanding this difference in performance.This is the motivating factor for our work.We begin by presenting $f$-MAX, a generalization of AIRL (Fu et al., 2018), a state-of-the-art IRL method.$f$-MAX provides grounds for more directly comparing the objectives for LfD.We demonstrate that $f$-MAX, and by inheritance AIRL, is a subset of the cost-regularized IRL framework laid out by Ho & Ermon (2016).We conclude by empirically evaluating the factors of difference between various LfD objectives in the continuous control domain.", "target": ["La corrispondenza della distribuzione attraverso la minimizzazione della divergenza fornisce un terreno comune per confrontare i metodi di reinforcement learning inverso a massima entropia avversaria con il Behaviour Cloning."]} +{"source": "Value-based methods constitute a fundamental methodology in planning and deep reinforcement learning (RL).In this paper, we propose to exploit the underlying structures of the state-action value function, i.e., Q function, for both planning and deep RL.In particular, if the underlying system dynamics lead to some global structures of the Q function, one should be capable of inferring the function better by leveraging such structures.Specifically, we investigate the low-rank structure, which widely exists for big data matrices.We verify empirically the existence of low-rank Q functions in the context of control and deep RL tasks (Atari games).As our key contribution, by leveraging Matrix Estimation (ME) techniques, we propose a general framework to exploit the underlying low-rank structure in Q functions, leading to a more efficient planning procedure for classical control, and additionally, a simple scheme that can be applied to any value-based RL techniques to consistently achieve better performance on ''low-rank'' tasks.Extensive experiments on control tasks and Atari games confirm the efficacy of our approach.", "target": ["Proponiamo un framework generico che permette di sfruttare la struttura a basso rango sia nella pianificazione sia nel deep reinforcement learning."]} +{"source": "Learned representations of source code enable various software developer tools, e.g., to detect bugs or to predict program properties.At the core of code representations often are word embeddings of identifier names in source code, because identifiers account for the majority of source code vocabulary and convey important semantic information.Unfortunately, there currently is no generally accepted way of evaluating the quality of word embeddings of identifiers, and current evaluations are biased toward specific downstream tasks.This paper presents IdBench, the first benchmark for evaluating to what extent word embeddings of identifiers represent semantic relatedness and similarity.The benchmark is based on thousands of ratings gathered by surveying 500 software developers.We use IdBench to evaluate state-of-the-art embedding techniques proposed for natural language, an embedding technique specifically designed for source code, and lexical string distance functions, as these are often used in current developer tools.Our results show that the effectiveness of embeddings varies significantly across different embedding techniques and that the best available embeddings successfully represent semantic relatedness.On the downside, no existing embedding provides a satisfactory representation of semantic similarities, e.g., because embeddings consider identifiers with opposing meanings as similar, which may lead to fatal mistakes in downstream developer tools.IdBench provides a gold standard to guide the development of novel embeddings that address the current limitations.", "target": ["Un benchmark per valutare gli embedding neurali degli identificatori nel codice sorgente."]} +{"source": "Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget.This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions.First, we argue that the existing MMD loss function may discourage the learning of fine details in data as it attempts to contract the discriminator outputs of real data.To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD.Second, inspired by the hinge loss, we propose a bounded Gaussian kernel to stabilize the training of MMD-GAN with the repulsive loss function.The proposed methods are applied to the unsupervised image generation tasks on CIFAR-10, STL-10, CelebA, and LSUN bedroom datasets.Results show that the repulsive loss function significantly improves over the MMD loss at no additional computational cost and outperforms other representative loss functions.The proposed methods achieve an FID score of 16.21 on the CIFAR-10 dataset using a single DCGAN network and spectral normalization.", "target": ["Riordinando i termini della maximum mean discrepancy si ottiene una loss molto migliore per il discriminatore delle reti generative adversarial"]} +{"source": "Deep neural networks have shown incredible performance for inference tasks in a variety of domains.Unfortunately, most current deep networks are enormous cloud-based structures that require significant storage space, which limits scaling of deep learning as a service (DLaaS) and use for on-device augmented intelligence. This paper finds algorithms that directly use lossless compressed representations of deep feedforward networks (with synaptic weights drawn from discrete sets), to perform inference without full decompression.The basic insight that allows less rate than naive approaches is the recognition that the bipartite graph layers of feedforward networks have a kind of permutation invariance to the labeling of nodes, in terms of inferential operation and that the inference operation depends locally on the edges directly connected to it.We also provide experimental results of our approach on the MNIST dataset.", "target": ["Questo articolo trova algoritmi che usano direttamente rappresentazioni compresse lossless di deep feedforward network, per eseguire l'inferenza senza decompressione completa."]} +{"source": "Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train.One common way to tackle this issue has been to propose new formulations of the GAN objective.Yet, surprisingly few studies have looked at optimization methods designed for this adversarial training.In this work, we cast GAN optimization problems in the general variational inequality framework.Tapping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend methods designed for variational inequalities to the training of GANs.We apply averaging, extrapolation and a computationally cheaper variant that we call extrapolation from the past to the stochastic gradient method (SGD) and Adam.", "target": ["Abbiamo applicato le GAN al framework della disuguaglianza variazionale e importiamo tecniche da questa letteratura per ottimizzare meglio le GAN; diamo estensioni algoritmiche e testiamo empiricamente le loro prestazioni per il training delle GAN."]} +{"source": "In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones.However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods.In addition, current task-specific meta-learning methods may either suffer from hand-crafted structure design or lack the capability to capture complex relations between tasks.In this paper, motivated by the way of knowledge organization in knowledge bases, we propose an automated relational meta-learning (ARML) framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph.When a new task arrives, it can quickly find the most relevant structure and tailor the learned structure knowledge to the meta-learner.As a result, the proposed framework not only addresses the challenge of task heterogeneity by a learned meta-knowledge graph, but also increases the model interpretability.We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.", "target": ["Affrontare il problema dell'eterogeneità dei task nel meta-learning introducendo il grafo della meta-conoscenza"]} +{"source": "In this paper, a deep boosting algorithm is developed tolearn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs (base experts)with diverse capabilities, e.g., these base deep CNNs aresequentially trained to recognize a set of object classes in an easy-to-hard way according to theirlearning complexities.Our experimental results have demonstratedthat our deep boosting algorithm can significantly improve theaccuracy rates on large-scale visual recognition.", "target": ["Un algoritmo di deep boosting viene sviluppato per imparare un classificatore ensemble più discriminante combinando senza soluzione di continuità un insieme di deep CNN di base."]} +{"source": "We present a method for translating music across musical instruments and styles.This method is based on unsupervised training of a multi-domain wavenet autoencoder, with a shared encoder and a domain-independent latent space that is trained end-to-end on waveforms.Employing a diverse training dataset and large net capacity, the single encoder allows us to translate also from musical domains that were not seen during training.We evaluate our method on a dataset collected from professional musicians, and achieve convincing translations.We also study the properties of the obtained translation and demonstrate translating even from a whistle, potentially enabling the creation of instrumental music by untrained humans.", "target": ["Un metodo automatico per convertire la musica tra strumenti e stili"]} +{"source": "Most existing defenses against adversarial attacks only consider robustness to L_p-bounded distortions.In reality, the specific attack is rarely known in advance and adversaries are free to modify images in ways which lie outside any fixed distortion model; for example, adversarial rotations lie outside the set of L_p-bounded distortions.In this work, we advocate measuring robustness against a much broader range of unforeseen attacks, attacks whose precise form is unknown during defense design.We propose several new attacks and a methodology for evaluating a defense against a diverse range of unforeseen distortions.First, we construct novel adversarial JPEG, Fog, Gabor, and Snow distortions to simulate more diverse adversaries.We then introduce UAR, a summary metric that measures the robustness of a defense against a given distortion. Using UAR to assess robustness against existing and novel attacks, we perform an extensive study of adversarial robustness.We find that evaluation against existing L_p attacks yields redundant information which does not generalize to other attacks; we instead recommend evaluating against our significantly more diverse set of attacks.We further find that adversarial training against either one or multiple distortions fails to confer robustness to attacks with other distortion types. These results underscore the need to evaluate and study robustness against unforeseen distortions.", "target": ["Proponiamo diversi nuovi attacchi e una metodologia per misurare la robustezza contro gli attacchi adversarial imprevisti."]} +{"source": "Deep neural networks (DNNs) have witnessed as a powerful approach in this year by solving long-standing Artificialintelligence (AI) supervised and unsupervised tasks exists in natural language processing, speech processing, computer vision and others.In this paper, we attempt to apply DNNs on three different cyber security use cases: Android malware classification, incident detection and fraud detection.The data set of each use case contains real known benign and malicious activities samples.These use cases are part of Cybersecurity Data Mining Competition (CDMC) 2017.The efficient network architecture for DNNs is chosen by conducting various trails of experiments for network parameters and network structures.The experiments of such chosen efficient configurations of DNNs are run up to 1000 epochs with learning rate set in the range [0.01-0.5].Experiments of DNNs performed well in comparison to the classical machine learning algorithm in all cases of experiments of cyber security use cases.This is due to the fact that DNNs implicitly extract and build better features, identifies the characteristics of the data that lead to better accuracy.The best accuracy obtained by DNNs and XGBoost on Android malware classification 0.940 and 0.741, incident detection 1.00 and 0.997, and fraud detection 0.972 and 0.916 respectively.The accuracy obtained by DNNs varies -0.05%, +0.02%, -0.01% from the top scored system in CDMC 2017 tasks.", "target": ["Deep-Net: deep neural network per i casi d'uso della sicurezza informatica"]} +{"source": "In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations.Current approaches to decomposing demonstrations into primitives often assume manually defined primitives and bypass the difficulty of discovering these primitives.On the other hand, approaches in primitive discovery put restrictive assumptions on the complexity of a primitive, which limit applicability to narrow tasks.Our approach attempts to circumvent these challenges by jointly learning both the underlying motor primitives and recomposing these primitives to form the original demonstration.Through constraints on both the parsimony of primitive decomposition and the simplicity of a given primitive, we are able to learn a diverse set of motor primitives, as well as a coherent latent representation for these primitives.We demonstrate, both qualitatively and quantitatively, that our learned primitives capture semantically meaningful aspects of a demonstration.This allows us to compose these primitives in a hierarchical reinforcement learning setup to efficiently solve robotic manipulation tasks like reaching and pushing.", "target": ["Impariamo uno spazio di primitive motorie da dimostrazioni robotiche non annotate, e dimostriamo che queste primitive sono semanticamente significative e possono essere composte per nuovi task robotici."]} +{"source": "Using modern deep learning models to make predictions on time series data from wearable sensors generally requires large amounts of labeled data.However, labeling these large datasets can be both cumbersome and costly.In this paper, we apply weak supervision to time series data, and programmatically label a dataset from sensors worn by patients with Parkinson's.We then built a LSTM model that predicts when these patients exhibit clinically relevant freezing behavior (inability to make effective forward stepping).We show that (1) when our model is trained using patient-specific data (prior sensor sessions), we come within 9% AUROC of a model trained using hand-labeled data and (2) when we assume no prior observations of subjects, our weakly supervised model matched performance with hand-labeled data.These results demonstrate that weak supervision may help reduce the need to painstakingly hand label time series training data.", "target": ["Dimostriamo la fattibilità di un approccio di classificazione delle serie temporali weakly supervised per i dati dei sensori indossabili."]} +{"source": "Learning semantic correspondence between the structured data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation.Recent neural generation methods require to use large scale training data.However, the collected data-text pairs for training are usually loosely corresponded, where texts contain additional or contradicted information compare to its paired input.In this paper, we propose a local-to-global alignment (L2GA) framework to learn semantic correspondences from loosely related data-text pairs.First, a local alignment model based on multi-instance learning is applied to build the semantic correspondences within a data-text pair.Then, a global alignment model built on top of a memory guided conditional random field (CRF) layer is designed to exploit dependencies among alignments in the entire training corpus, where the memory is used to integrate the alignment clues provided by the local alignment model.Therefore, it is capable of inducing missing alignments for text spans that are not supported by its imperfect paired input.Experiments on recent restaurant dataset show that our proposed method can improve the alignment accuracy and as a by product, our method is also applicable to induce semantically equivalent training data-text pairs for neural generation models.", "target": ["Proponiamo un framework di allineamento locale-globale per imparare corrispondenze semantiche da coppie di dati-testo rumorose con una weak supervision"]} +{"source": "Imitation learning algorithms provide a simple and straightforward approach for training control policies via standard supervised learning methods.By maximizing the likelihood of good actions provided by an expert demonstrator, supervised imitation learning can produce effective policies without the algorithmic complexities and optimization challenges of reinforcement learning, at the cost of requiring an expert demonstrator -- typically a person -- to provide the demonstrations.In this paper, we ask: can we use imitation learning to train effective policies without any expert demonstrations?The key observation that makes this possible is that, in the multi-task setting, trajectories that are generated by a suboptimal policy can still serve as optimal examples for other tasks.In particular, in the setting where the tasks correspond to different goals, every trajectory is a successful demonstration for the state that it actually reaches.Informed by this observation, we propose a very simple algorithm for learning behaviors without any demonstrations, user-provided reward functions, or complex reinforcement learning methods.Our method simply maximizes the likelihood of actions the agent actually took in its own previous rollouts, conditioned on the goal being the state that it actually reached.Although related variants of this approach have been proposed previously in imitation learning settings with example demonstrations, we present the first instance of this approach as a method for learning goal-reaching policies entirely from scratch.We present a theoretical result linking self-supervised imitation learning and reinforcement learning, and empirical results showing that it performs competitively with more complex reinforcement learning methods on a range of challenging goal reaching problems.", "target": ["Imparare a raggiungere gli obiettivi da zero usando l'imitation learning con la riannotazione dei dati"]} +{"source": "Neural networks have recently shown excellent performance on numerous classi- fication tasks.These networks often have a large number of parameters and thus require much data to train.When the number of training data points is small, however, a network with high flexibility will quickly overfit the training data, resulting in a large model variance and a poor generalization performance.To address this problem, we propose a new ensemble learning method called InterBoost for small-sample image classification.In the training phase, InterBoost first randomly generates two complementary datasets to train two base networks of the same structure, separately, and then next two complementary datasets for further training the networks are generated through interaction (or information sharing) between the two base networks trained previously.This interactive training process continues iteratively until a stop criterion is met.In the testing phase, the outputs of the two networks are combined to obtain one final score for classification.Detailed analysis of the method is provided for an in-depth understanding of its mechanism.", "target": ["Nell'articolo, abbiamo proposto un metodo di ensemble chiamato InterBoost per il training di reti neurali per la classificazione di piccoli sample. Il metodo ha migliori prestazioni di generalizzazione rispetto ad altri metodi d'insieme e riduce significativamente le varianze."]} +{"source": "Interpreting neural networks is a crucial and challenging task in machine learning.In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights.Depending on the desired interactions, our method can achieve significantly better or similar interaction detection performance compared to the state-of-the-art without searching an exponential solution space of possible interactions.We obtain this accuracy and efficiency by observing that interactions between input features are created by the non-additive effect of nonlinear activation functions, and that interacting paths are encoded in weight matrices.We demonstrate the performance of our method and the importance of discovered interactions via experimental results on both synthetic datasets and real-world application datasets.", "target": ["Rileviamo le interazioni statistiche catturate da una rete neurale multilayer feedforward interpretando direttamente i suoi pesi appresi."]} +{"source": "The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforcement learning.Despite its apparent successes in these settings, to the best of our knowledge there has been no systematic exploration of its capabilities on simple regression tasks.In this work we characterize these on the UCI datasets, a popular benchmark for Bayesian regression models, as well as on the recently introduced ''gap'' datasets, which are better tests of out-of-distribution uncertainty.We demonstrate that the neural linear model is a simple method that shows competitive performance on these tasks.", "target": ["Facciamo un benchmark del modello lineare neurale sui dataset UCI e UCI \"gap\"."]} +{"source": "The reproducibility of reinforcement-learning research has been highlighted as a key challenge area in the field.In this paper, we present a case study in reproducing the results of one groundbreaking algorithm, AlphaZero, a reinforcement learning system that learns how to play Go at a superhuman level given only the rules of the game.We describe Minigo, a reproduction of the AlphaZero system using publicly available Google Cloud Platform infrastructure and Google Cloud TPUs.The Minigo system includes both the central reinforcement learning loop as well as auxiliary monitoring and evaluation infrastructure.With ten days of training from scratch on 800 Cloud TPUs, Minigo can play evenly against LeelaZero and ELF OpenGo, two of the strongest publicly available Go AIs.We discuss the difficulties of scaling a reinforcement learning system and the monitoring systems required to understand the complex interplay of hyperparameter configurations.", "target": ["Abbiamo riprodotto AlphaZero su Google Cloud Platform"]} +{"source": "Generative adversarial networks (GANs) train implicit generative models through solving minimax problems.Such minimax problems are known as nonconvex- nonconcave, for which the dynamics of first-order methods are not well understood.In this paper, we consider GANs in the type of the integral probability metrics (IPMs) with the generator represented by an overparametrized neural network.When the discriminator is solved to approximate optimality in each iteration, we prove that stochastic gradient descent on a regularized IPM objective converges globally to a stationary point with a sublinear rate.Moreover, we prove that when the width of the generator network is sufficiently large and the discriminator function class has enough discriminative ability, the obtained stationary point corresponds to a generator that yields a distribution that is close to the distribution of the observed data in terms of the total variation.To the best of our knowledge, we seem to first establish both the global convergence and global optimality of training GANs when the generator is parametrized by a neural network.", "target": ["Stabiliamo la convergenza globale all'ottimalità per le GAN basate su IPM dove il generatore è una rete neurale iperparametrizzata."]} +{"source": "We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram.Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE). Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes.We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings.Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social, web and citation network datasets.", "target": ["Sviluppiamo efficienti procedure di inclusione di reti approssimate su più scale con proprietà dimostrabili."]} +{"source": "Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difficult.In this paper, we present1) a consistent comparative analysis of several representative few-shot classification algorithms, with results showing that deeper backbones significantly reduce the gap across methods including the baseline,2) a slightly modified baseline method that surprisingly achieves competitive performance when compared with the state-of-the-art on both the mini-ImageNet and the CUB datasets, and3) a new experimental setting for evaluating the cross-domain generalization ability for few-shot classification algorithms.Our results reveal that reducing intra-class variation is an important factor when the feature backbone is shallow, but not as critical when using deeper backbones.In a realistic, cross-domain evaluation setting, we show that a baseline method with a standard fine-tuning practice compares favorably against other state-of-the-art few-shot learning algorithms.", "target": ["Uno studio empirico dettagliato nella classificazione few-shot che rivela le sfide nel setting di valutazione standard e mostra una nuova direzione."]} +{"source": "Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning.Prior works on inferring temporal logic specifications have focused on \"summarizing\" the input dataset -- i.e., finding specifications that are satisfied by all plan traces belonging to the given set.In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces.We formalize the concept of providing such contrastive explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications.We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air combat mission.", "target": ["Presentiamo un modello di inferenza bayesiana per dedurre spiegazioni contrastive (come specifiche LTL) che descrivono come due set di plan trace differiscono."]} +{"source": "This work tackles the problem of characterizing and understanding the decision boundaries of neural networks with piece-wise linear non-linearity activations.We use tropical geometry, a new development in the area of algebraic geometry, to provide a characterization of the decision boundaries of a simple neural network of the form (Affine, ReLU, Affine).Specifically, we show that the decision boundaries are a subset of a tropical hypersurface, which is intimately related to a polytope formed by the convex hull of two zonotopes.The generators of the zonotopes are precise functions of the neural network parameters.We utilize this geometric characterization to shed light and new perspective on three tasks.In doing so, we propose a new tropical perspective for the lottery ticket hypothesis, where we see the effect of different initializations on the tropical geometric representation of the decision boundaries.Also, we leverage this characterization as a new set of tropical regularizers, which deal directly with the decision boundaries of a network.We investigate the use of these regularizers in neural network pruning (removing network parameters that do not contribute to the tropical geometric representation of the decision boundaries) and in generating adversarial input attacks (with input perturbations explicitly perturbing the decision boundaries geometry to change the network prediction of the input).", "target": ["La geometria tropicale può essere sfruttata per rappresentare i confini decisionali delle reti neurali e portare alla luce intuizioni interessanti."]} +{"source": "First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks.Second-order methods, despite their better convergence rate, are rarely used in practice due to the pro- hibitive computational cost in calculating the second-order information.In this paper, we propose a novel Gram-Gauss-Newton (GGN) algorithm to train deep neural networks for regression problems with square loss.Our method draws inspiration from the connection between neural network optimization and kernel regression of neural tangent kernel (NTK).Different from typical second-order methods that have heavy computational cost in each iteration, GGN only has minor overhead compared to first-order methods such as SGD.We also give theoretical results to show that for sufficiently wide neural networks, the convergence rate of GGN is quadratic.Furthermore, we provide convergence guarantee for mini-batch GGN algorithm, which is, to our knowledge, the first convergence result for the mini-batch version of a second-order method on overparameterized neural net- works.Preliminary experiments on regression tasks demonstrate that for training standard networks, our GGN algorithm converges much faster and achieves better performance than SGD.", "target": ["Un nuovo metodo Gram-Gauss-Newton per addestrare le reti neurali, ispirato dal kernel della tangente neurale e dal metodo Gauss-Newton, con velocità di convergenza veloce sia teoricamente che sperimentalmente."]} +{"source": "Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures?We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018), which we use to investigate the grammatical knowledge of three pretrained encoders, including the popular OpenAI Transformer (Radford et al., 2018) and BERT (Devlin et al., 2018).We fine-tune these encoders to do acceptability classification over CoLA and compare the models’ performance on the annotated analysis set.Some phenomena, e.g. modification by adjuncts, are easy to learn for all models, while others, e.g. long-distance movement, are learned effectively only by models with strong overall performance, and others still, e.g. morphological agreement, are hardly learned by any model.", "target": ["Indaghiamo la conoscenza sintattica implicita degli embedding di frasi usando un nuovo set di analisi di frasi annotate grammaticalmente con giudizi di accettabilità."]} +{"source": "When considering simultaneously a finite number of tasks, multi-output learning enables one to account for the similarities of the tasks via appropriate regularizers.We propose a generalization of the classical setting to a continuum of tasks by using vector-valued RKHSs.", "target": ["Proponiamo un'estensione dell'apprendimento multi-output ad un continuum di task utilizzando kernel valutati dall'operatore."]} +{"source": "We analyze the joint probability distribution on the lengths of thevectors of hidden variables in different layers of a fully connecteddeep network, when the weights and biases are chosen randomly according toGaussian distributions, and the input is binary-valued. We showthat, if the activation function satisfies a minimal set ofassumptions, satisfied by all activation functions that we know thatare used in practice, then, as the width of the network gets large,the ``length process'' converges in probability to a length mapthat is determined as a simple function of the variances of therandom weights and biases, and the activation function.We also show that this convergence may fail for activation functions that violate our assumptions.", "target": ["Dimostriamo che, per funzioni di attivazione che soddisfano alcune condizioni, man mano che una deep network si allarga, le lunghezze dei vettori delle variabili nascoste convergono verso una mappa di lunghezza."]} +{"source": "Data augmentation is one of the most effective approaches for improving the accuracy of modern machine learning models, and it is also indispensable to train a deep model for meta-learning.However, most current data augmentation implementations applied in meta-learning are the same as those used in the conventional image classification.In this paper, we introduce a new data augmentation method for meta-learning, which is named as ``Task Level Data Augmentation'' (referred to Task Aug).The basic idea of Task Aug is to increase the number of image classes rather than the number of images in each class.In contrast, with a larger amount of classes, we can sample more diverse task instances during training.This allows us to train a deep network by meta-learning methods with little over-fitting.Experimental results show that our approach achieves state-of-the-art performance on miniImageNet, CIFAR-FS, and FC100 few-shot learning benchmarks.Once paper is accepted, we will provide the link to code.", "target": ["Proponiamo un approccio di data augmentation per meta-learning e dimostriamo che è valido."]} +{"source": "In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network, significantly extending prior work on a method known as ``Bayesian Dark Knowledge.\" Our generalized framework applies to the case of classification models and takes as input the architecture of a ``teacher\" network, a general posterior expectation of interest, and the architecture of a ``student\" network.The distillation method performs an online compression of the selected posterior expectation using iteratively generated Monte Carlo samples from the parameter posterior of the teacher model.We further consider the problem of optimizing the student model architecture with respect to an accuracy-speed-storage trade-off.We present experimental results investigating multiple data sets, distillation targets, teacher model architectures, and approaches to searching for student model architectures.We establish the key result that distilling into a student model with an architecture that matches the teacher, as is done in Bayesian Dark Knowledge, can lead to sub-optimal performance.Lastly, we show that student architecture search methods can identify student models with significantly improved performance.", "target": ["Un framework generale per distillare le aspettative posteriori bayesiane per le deep neural network."]} +{"source": "Variational Autoencoders (VAEs) have proven to be powerful latent variable models.How- ever, the form of the approximate posterior can limit the expressiveness of the model.Categorical distributions are flexible and useful building blocks for example in neural memory layers.We introduce the Hierarchical Discrete Variational Autoencoder (HD-VAE): a hi- erarchy of variational memory layers.The Concrete/Gumbel-Softmax relaxation allows maximizing a surrogate of the Evidence Lower Bound by stochastic gradient ascent.We show that, when using a limited number of latent variables, HD-VAE outperforms the Gaussian baseline on modelling multiple binary image datasets.Training very deep HD-VAE remains a challenge due to the relaxation bias that is induced by the use of a surrogate objective.We introduce a formal definition and conduct a preliminary theoretical and empirical study of the bias.", "target": ["In questo articolo, introduciamo una gerarchia discreta di variabili latenti categoriche che addestriamo usando il rilassamento Concrete/Gumbel-Softmax e deriviamo un limite superiore per la differenza assoluta tra l'obiettivo biased e quello unbiased."]} +{"source": "In this paper, we propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term \\emph{PowerSGD}.The proposed PowerSGD method simply raises the stochastic gradient to a certain power $\\gamma\\in[0,1]$ during iterations and introduces only one additional parameter, namely, the power exponent $\\gamma$ (when $\\gamma=1$, PowerSGD reduces to SGD).We further propose PowerSGD with momentum, which we term \\emph{PowerSGDM}, and provide convergence rate analysis on both PowerSGD and PowerSGDM methods.Experiments are conducted on popular deep learning models and benchmark datasets.Empirical results show that the proposed PowerSGD and PowerSGDM obtain faster initial training speed than adaptive gradient methods, comparable generalization ability with SGD, and improved robustness to hyper-parameter selection and vanishing gradients.PowerSGD is essentially a gradient modifier via a nonlinear transformation.As such, it is orthogonal and complementary to other techniques for accelerating gradient-based optimization.", "target": ["Proponiamo una nuova classe di ottimizzatori per l'ottimizzazione accelerata non convessa tramite una trasformazione non lineare del gradiente."]} +{"source": "We aim to build complex humanoid agents that integrate perception, motor control, and memory.In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision.We develop an architecture capable of surprisingly flexible, task-directed motor control of a relatively high-DoF humanoid body by combining pre-training of low-level motor controllers with a high-level, task-focused controller that switches among low-level sub-policies.The resulting system is able to control a physically-simulated humanoid body to solve tasks that require coupling visual perception from an unstabilized egocentric RGB camera during locomotion in the environment.Supplementary video link: https://youtu.be/fBoir7PNxPk", "target": ["Risolvere task che coinvolgono la locomozione umanoide guidata dalla visione, riutilizzando il comportamento di locomozione dai dati di motion capture."]} +{"source": "The gap between the empirical success of deep learning and the lack of strong theoretical guarantees calls for studying simpler models.By observing that a ReLU neuron is a product of a linear function with a gate (the latter determines whether the neuron is active or not), where both share a jointly trained weight vector, we propose to decouple the two.We introduce GaLU networks — networks in which each neuron is a product of a Linear Unit, defined by a weight vector which is being trained, with a Gate, defined by a different weight vector which is not being trained.Generally speaking, given a base model and a simpler version of it, the two parameters that determine the quality of the simpler version are whether its practical performance is close enough to the base model and whether it is easier to analyze it theoretically.We show that GaLU networks perform similarly to ReLU networks on standard datasets and we initiate a study of their theoretical properties, demonstrating that they are indeed easier to analyze.We believe that further research of GaLU networks may be fruitful for the development of a theory of deep learning.", "target": ["Proponiamo le reti Gated Linear Unit - un modello che si comporta in modo simile alle reti ReLU su dati reali, pur essendo molto più facile da analizzare teoricamente."]} +{"source": "Machine learning systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a different distribution from the one used for training.With their growing use in critical applications, it becomes important to develop systems that are able to accurately quantify its predictive uncertainty and screen out these anomalous inputs.However, unlike standard learning tasks, there is currently no well established guiding principle for designing architectures that can accurately quantify uncertainty.Moreover, commonly used OoD detection approaches are prone to errors and even sometimes assign higher likelihoods to OoD samples.To address these problems, we first seek to identify guiding principles for designing uncertainty-aware architectures, by proposing Neural Architecture Distribution Search (NADS).Unlike standard neural architecture search methods which seek for a single best performing architecture, NADS searches for a distribution of architectures that perform well on a given task, allowing us to identify building blocks common among all uncertainty aware architectures.With this formulation, we are able to optimize a stochastic outlier detection objective and construct an ensemble of models to perform OoD detection.We perform multiple OoD detection experiments and observe that our NADS performs favorably compared to state-of-the-art OoD detection methods.", "target": ["Proponiamo un metodo di ricerca dell'architettura per identificare una distribuzione di architetture e usarla per costruire un insieme bayesiano per il rilevamento degli outlier."]} +{"source": "Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data.In this work we propose a novel image outlier detection approach (IOD for short) that leverages the cutting-edge image classifier to discover outliers without using any labeled outlier.We observe that although intuitively the confidence that a convolutional neural network (CNN) has that an image belongs to a particular class could serve as outlierness measure to each image, directly applying this confidence to detect outlier does not work well.This is because CNN often has high confidence on an outlier image that does not belong to any target class due to its generalization ability that ensures the high accuracy in classification.To solve this issue, we propose a Deep Neural Forest-based approach that harmonizes the contradictory requirements of accurately classifying images and correctly detecting the outlier images.Our experiments using several benchmark image datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN demonstrate the effectiveness of our IOD approach for outlier detection, capturing more than 90% of outliers generated by injecting one image dataset into another, while still preserving the classification accuracy of the multi-class classification problem.", "target": ["Un nuovo approccio che rileva i valori anomali dai dati di immagine, preservando l'accuratezza della classificazione delle immagini"]} +{"source": "This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources.We design a Dynamic Point-cloud Convolution (D-Conv) operator as the core component of CloudLSTMs, which performs convolution directly over point-clouds and extracts local spatial features from sets of neighboring points that surround different elements of the input.This operator maintains the permutation invariance of sequence-to-sequence learning frameworks, while representing neighboring correlations at each time step -- an important aspect in spatiotemporal predictive learning.The D-Conv operator resolves the grid-structural data requirements of existing spatiotemporal forecasting models and can be easily plugged into traditional LSTM architectures with sequence-to-sequence learning and attention mechanisms. We apply our proposed architecture to two representative, practical use cases that involve point-cloud streams, i.e. mobile service traffic forecasting and air quality indicator forecasting.Our results, obtained with real-world datasets collected in diverse scenarios for each use case, show that CloudLSTM delivers accurate long-term predictions, outperforming a variety of neural network models.", "target": ["Questo articolo introduce CloudLSTM, un nuovo tipo di modelli neurali ricorrenti su misura per la predizione su flussi di dati generati da fonti geospaziali point-cloud."]} +{"source": "Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge.For easy access to statistical approaches on relational data, multiple methods to embed a KG as components of R^d have been introduced.We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space.TransINT maps set of entities (tied by a relation) to continuous sets of vectors that are inclusion-ordered isomorphically to relation implications.With a novel parameter sharing scheme, TransINT enables automatic training on missing but implied facts without rule grounding.We achieve new state-of-the-art performances with signficant margins in Link Prediction and Triple Classification on FB122 dataset, with boosted performance even on test instances that cannot be inferred by logical rules.The angles between the continuous sets embedded by TransINT provide an interpretable way to mine semantic relatedness and implication rules among relations.", "target": ["Proponiamo TransINT, un metodo di embedding KG nuovo e interpretabile che conserva isomorficamente l'ordine di implicazione tra le relazioni nello spazio di embedding in un modo spiegabile, robusto e geometricamente coerente."]} +{"source": "Unsupervised domain adaptive object detection aims to learn a robust detector on the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different.In this paper, we propose a gradient detach based Stacked Complementary Losses (SCL) method that uses detection objective (cross entropy and smooth l1 regression) as the primary objective, and cuts in several auxiliary losses in different network stages to utilize information from the complement data (target images) that can be effective in adapting model parameters to both source and target domains.A gradient detach operation is applied between detection and context sub-networks during training to force networks to learn discriminative representations.We argue that the conventional training with primary objective mainly leverages the information from the source-domain for maximizing likelihood and ignores the complement data in shallow layers of networks, which leads to an insufficient integration within different domains.Thus, our proposed method is a more syncretic adaptation learning process.We conduct comprehensive experiments on seven datasets, the results demonstrate that our method performs favorably better than the state-of-the-art methods by a large margin.For instance, from Cityscapes to FoggyCityscapes, we achieve 37.9% mAP, outperforming the previous art Strong-Weak by 3.6%.", "target": ["Introduciamo una nuova strategia di training con obiettivo complementare basata sul gradiente per la rilevazione adattiva degli oggetti nel dominio."]} +{"source": "Convolutional neural networks (CNN) have become the most successful and popular approach in many vision-related domains.While CNNs are particularly well-suited for capturing a proper hierarchy of concepts from real-world images, they are limited to domains where data is abundant.Recent attempts have looked into mitigating this data scarcity problem by casting their original single-task problem into a new multi-task learning (MTL) problem.The main goal of this inductive transfer mechanism is to leverage domain-specific information from related tasks, in order to improve generalization on the main task.While recent results in the deep learning (DL) community have shown the promising potential of training task-specific CNNs in a soft parameter sharing framework, integrating the recent DL advances for improving knowledge sharing is still an open problem.In this paper, we propose the Deep Collaboration Network (DCNet), a novel approach for connecting task-specific CNNs in a MTL framework.We define connectivity in terms of two distinct non-linear transformation blocks.One aggregates task-specific features into global features, while the other merges back the global features with each task-specific network.Based on the observation that task relevance depends on depth, our transformation blocks use skip connections as suggested by residual network approaches, to more easily deactivate unrelated task-dependent features.To validate our approach, we employed facial landmark detection (FLD) datasets as they are readily amenable to MTL, given the number of tasks they include.Experimental results show that we can achieve up to 24.31% relative improvement in landmark failure rate over other state-of-the-art MTL approaches.We finally perform an ablation study showing that our approach effectively allows knowledge sharing, by leveraging domain-specific features at particular depths from tasks that we know are related.", "target": ["Proponiamo un nuovo approccio per collegare le reti specifiche di un task in un setting di multi-task learning basato su recenti progressi delle reti con residual connection."]} +{"source": "Zero-Shot Learning (ZSL) is a classification task where some classes referred as unseen classes have no labeled training images.Instead, we only have side information (or description) about seen and unseen classes, often in the form of semantic or descriptive attributes.Lack of training images from a set of classes restricts the use of standard classification techniques and losses, including the popular cross-entropy loss.The key step in tackling ZSL problem is bridging visual to semantic space via learning a nonlinear embedding.A well established approach is to obtain the semantic representation of the visual information and perform classification in the semantic space.In this paper, we propose a novel architecture of casting ZSL as a fully connected neural-network with cross-entropy loss to embed visual space to semantic space.During training in order to introduce unseen visual information to the network, we utilize soft-labeling based on semantic similarities between seen and unseen classes.To the best of our knowledge, such similarity based soft-labeling is not explored for cross-modal transfer and ZSL.We evaluate the proposed model on five benchmark datasets for zero-shot learning, AwA1, AwA2, aPY, SUN and CUB datasets, and show that, despite the simplicity, our approach achieves the state-of-the-art performance in Generalized-ZSL setting on all of these datasets and outperforms the state-of-the-art for some datasets.", "target": ["Come usare la cross-entropy loss per zero shot learning con soft labelling su classi non viste: una soluzione semplice ed efficace che raggiunge prestazioni all'avanguardia su cinque dataset di riferimento ZSL."]} +{"source": "In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress.In this work, we use a curriculum of progressively growing action spaces to accelerate learning.We assume the environment is out of our control, but that the agent may set an internal curriculum by initially restricting its action space.Our approach uses off-policy reinforcement learning to estimate optimal value functions for multiple action spaces simultaneously and efficiently transfers data, value estimates, and state representations from restricted action spaces to the full task.We show the efficacy of our approach in proof-of-concept control tasks and on challenging large-scale StarCraft micromanagement tasks with large, multi-agent action spaces.", "target": ["Far crescere progressivamente lo spazio d'azione disponibile è un ottimo curriculum per gli agenti di apprendimento"]} +{"source": "Recently, researchers have discovered that the state-of-the-art object classifiers can be fooled easily by small perturbations in the input unnoticeable to human eyes. It is known that an attacker can generate strong adversarial examples if she knows the classifier parameters. Conversely, a defender can robustify the classifier by retraining if she has the adversarial examples. The cat-and-mouse game nature of attacks and defenses raises the question of the presence of equilibria in the dynamics.In this paper, we present a neural-network based attack class to approximate a larger but intractable class of attacks, and formulate the attacker-defender interaction as a zero-sum leader-follower game.We present sensitivity-penalized optimization algorithms to find minimax solutions, which are the best worst-case defenses against whitebox attacks.Advantages of the learning-based attacks and defenses compared to gradient-based attacks and defenses are demonstrated with MNIST and CIFAR-10.", "target": ["Una soluzione game-theoretic agli attacchi e alle difese adversarial."]} +{"source": "Supervised learning with irregularly sampled time series have been a challenge to Machine Learning methods due to the obstacle of dealing with irregular time intervals.Some papers introduced recently recurrent neural network models that deals with irregularity, but most of them rely on complex mechanisms to achieve a better performance.This work propose a novel method to represent timestamps (hours or dates) as dense vectors using sinusoidal functions, called Time Embeddings.As a data input method it and can be applied to most machine learning models.The method was evaluated with two predictive tasks from MIMIC III, a dataset of irregularly sampled time series of electronic health records.Our tests showed an improvement to LSTM-based and classical machine learning models, specially with very irregular data.", "target": ["Un nuovo metodo per creare descrittori densi di tempo (Time Embeddings) per far capire le strutture temporali a modelli semplici"]} +{"source": "Community detection in graphs can be solved via spectral methods or posterior inference under certain probabilistic graphical models.Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational detection thresholds in terms of the signal-to-noise ratio.By recasting community detection as a node-wise classification problem on graphs, we can also study it from a learning perspective.We present a novel family of Graph Neural Networks (GNNs) for solving community detection problems in a supervised learning setting.We show that, in a data-driven manner and without access to the underlying generative models, they can match or even surpass the performance of the belief propagation algorithm on binary and multiclass stochastic block models, which is believed to reach the computational threshold in these cases.In particular, we propose to augment GNNs with the non-backtracking operator defined on the line graph of edge adjacencies.The GNNs are achieved good performance on real-world datasets. In addition, we perform the first analysis of the optimization landscape of using (linear) GNNs to solve community detection problems, demonstrating that under certain simplifications and assumptions, the loss value at any local minimum is close to the loss value at the global minimum/minima.", "target": ["Proponiamo una nuova architettura di graph neural network basata sulla matrice di non-backtracking definita sulle adiacenze degli archi e dimostriamo la sua efficacia nei task di rilevamento della comunità sui grafi."]} +{"source": "Residual networks (Resnets) have become a prominent architecture in deep learning.However, a comprehensive understanding of Resnets is still a topic of ongoing research.A recent view argues that Resnets perform iterative refinement of features.We attempt to further expose properties of this aspect.To this end, we study Resnets both analytically and empirically.We formalize the notion of iterative refinement in Resnets by showing that residual architectures naturally encourage features to move along the negative gradient of loss during the feedforward phase.In addition, our empirical analysis suggests that Resnets are able to perform both representation learning and iterative refinement.In general, a Resnet block tends to concentrate representation learning behavior in the first few layers while higher layers perform iterative refinement of features.Finally we observe that sharing residual layers naively leads to representation explosion and hurts generalization performance, and show that simple existing strategies can help alleviating this problem.", "target": ["Le residual connection eseguono davvero un'inferenza iterativa"]} +{"source": "We develop end-to-end learned reconstructions for lensless mask-based cameras, including an experimental system for capturing aligned lensless and lensed images for training. Various reconstruction methods are explored, on a scale from classic iterative approaches (based on the physical imaging model) to deep learned methods with many learned parameters. In the middle ground, we present several variations of unrolled alternating direction method of multipliers (ADMM) with varying numbers of learned parameters.The network structure combines knowledge of the physical imaging model with learned parameters updated from the data, which compensate for artifacts caused by physical approximations.Our unrolled approach is 20X faster than classic methods and produces better reconstruction quality than both the classic and deep methods on our experimental system.", "target": ["Miglioriamo il tempo e la qualità della ricostruzione su un imager sperimentale senza maschera utilizzando un approccio di apprendimento end-to-end che incorpora la conoscenza del modello di imaging."]} +{"source": "Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years.With multiple hidden layers, deep learning models aim at computing the hierarchical feature representations of the observational data.Meanwhile, due to its severe disadvantages in data consumption, computational resources, parameter tuning costs and the lack of result explainability, deep learning has also suffered from lots of criticism.In this paper, we will introduce a new representation learning model, namely “Sample-Ensemble Genetic Evolutionary Network” (SEGEN), which can serve as an alternative approach to deep learning models.Instead of building one single deep model, based on a set of sampled sub-instances, SEGEN adopts a genetic-evolutionary learning strategy to build a group of unit models generations by generations.The unit models incorporated in SEGEN can be either traditional machine learning models or the recent deep learning models with a much “narrower” and “shallower” architecture.The learning results of each instance at the final generation will be effectively combined from each unit model via diffusive propagation and ensemble learning strategies.From the computational perspective, SEGEN requires far less data, fewer computational resources and parameter tuning efforts, but has sound theoretic interpretability of the learning process and results.Extensive experiments have been done on several different real-world benchmark datasets, and the experimental results obtained by SEGEN have demonstrated its advantages over the state-of-the-art representation learning models.", "target": ["Introduciamo un nuovo modello di representation learning, cioè \"Sample-Ensemble Genetic Evolutionary Network\" (SEGEN), che può servire come un approccio alternativo ai modelli di deep learning."]} +{"source": "How can we teach artificial agents to use human language flexibly to solve problems in a real-world environment?We have one example in nature of agents being able to solve this problem: human babies eventually learn to use human language to solve problems, and they are taught with an adult human-in-the-loop.Unfortunately, current machine learning methods (e.g. from deep reinforcement learning) are too data inefficient to learn a language in this way (3).An outstanding goal is finding an algorithm with a suitable ‘language learning prior’ that allows it to learn human language, while minimizing the number of required human interactions.In this paper, we propose to learn such a prior in simulation, leveraging the increasing amount of available compute for machine learning experiments (1).We call our approach Learning to Learn to Communicate (L2C).Specifically, in L2C we train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol.Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations unseen during training, including populations of humans.To show the promise of the L2C framework, we conduct some preliminary experiments in a Lewis signaling game (4), where we show that agentstrained with L2C are able to learn a simple form of human language (represented by a hand-coded compositional language) in fewer iterations than randomly initialized agents.", "target": ["Proponiamo di utilizzare il meta-learning per un apprendimento linguistico più efficiente, attraverso una sorta di \"randomizzazione del dominio\"."]} +{"source": "We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization. This allows us to explicitly search for schedules that achieve good generalization.We describe the structure of the gradient of a validation error w.r.t.the learning rates, the hypergradient, and based on this we introduce a novel online algorithm.Our method adaptively interpolates between two recently proposed techniques (Franceschi et al., 2017; Baydin et al.,2018), featuring increased stability and faster convergence.We show empirically that the proposed technique compares favorably with baselines and related methodsin terms of final test accuracy.", "target": ["MARTHE: un nuovo metodo per adattare le schedule di learning rate specifici del task dal punto di vista dell'ottimizzazione degli iperparametri"]} +{"source": "Recent years have witnessed some exciting developments in the domain of generating images from scene-based text descriptions.These approaches have primarily focused on generating images from a static text description and are limited to generating images in a single pass.They are unable to generate an image interactively based on an incrementally additive text description (something that is more intuitive and similar to the way we describe an image). We propose a method to generate an image incrementally based on a sequence of graphs of scene descriptions (scene-graphs).We propose a recurrent network architecture that preserves the image content generated in previous steps and modifies the cumulative image as per the newly provided scene information.Our model utilizes Graph Convolutional Networks (GCN) to cater to variable-sized scene graphs along with Generative Adversarial image translation networks to generate realistic multi-object images without needing any intermediate supervision during training.We experiment with Coco-Stuff dataset which has multi-object images along with annotations describing the visual scene and show that our model significantly outperforms other approaches on the same dataset in generating visually consistent images for incrementally growing scene graphs.", "target": ["Generazione interattiva di immagini da grafi di scena in crescita incrementale in più step utilizzando GAN mentre si conservano i contenuti dell'immagine generata negli step precedenti"]} +{"source": "In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images.However, most Convolutional Neural Networks (CNNs) for image classification were developed using biased datasets that contain large objects, in mostly central image positions.To assess whether classical CNN architectures work well for tiny object classification we build a comprehensive testbed containing two datasets: one derived from MNIST digits and one from histopathology images.This testbed allows controlled experiments to stress-test CNN architectures with a broad spectrum of signal-to-noise ratios.Our observations indicate that: (1) There exists a limit to signal-to-noise below which CNNs fail to generalize and that this limit is affected by dataset size - more data leading to better performances; however, the amount of training data required for the model to generalize scales rapidly with the inverse of the object-to-image ratio (2) in general, higher capacity models exhibit better generalization; (3) when knowing the approximate object sizes, adapting receptive field is beneficial; and (4) for very small signal-to-noise ratio the choice of global pooling operation affects optimization, whereas for relatively large signal-to-noise values, all tested global pooling operations exhibit similar performance.", "target": ["Studiamo scenari di classificazione a basso e bassissimo rapporto segnale-rumore, dove gli oggetti che si correlano con la label di classe occupano una piccola parte dell'intera immagine (ad esempio, immagini mediche o iperspettrali)."]} +{"source": "Recent trends of incorporating attention mechanisms in vision have led researchers to reconsider the supremacy of convolutional layers as a primary building block.Beyond helping CNNs to handle long-range dependencies, Ramachandran et al. (2019) showed that attention can completely replace convolution and achieve state-of-the-art performance on vision tasks.This raises the question: do learned attention layers operate similarly to convolutional layers?This work provides evidence that attention layers can perform convolution and, indeed, they often learn to do so in practice.Specifically, we prove that a multi-head self-attention layer with sufficient number of heads is at least as expressive as any convolutional layer.Our numerical experiments then show that self-attention layers attend to pixel-grid patterns similarly to CNN layers, corroborating our analysis.Our code is publicly available.", "target": ["Un layer di self-attention può eseguire la convoluzione e spesso impara a farlo nella pratica."]} +{"source": "We introduce a “learning-based” algorithm for the low-rank decomposition problem: given an $n \\times d$ matrix $A$, and a parameter $k$, compute a rank-$k$ matrix $A'$ that minimizes the approximation loss $||A- A'||_F$.The algorithm uses a training set of input matrices in order to optimize its performance.Specifically, some of the most efficient approximate algorithms for computing low-rank approximations proceed by computing a projection $SA$, where $S$ is a sparse random $m \\times n$ “sketching matrix”, and then performing the singular value decomposition of $SA$.We show how to replace the random matrix $S$ with a “learned” matrix of the same sparsity to reduce the error.Our experiments show that, for multiple types of data sets, a learned sketch matrix can substantially reduce the approximation loss compared to a random matrix $S$, sometimes by one order of magnitude.We also study mixed matrices where only some of the rows are trained and the remaining ones are random, and show that matrices still offer improved performance while retaining worst-case guarantees.", "target": ["Gli algoritmi basati sull'apprendimento possono migliorare le prestazioni degli algoritmi classici per il problema dell'approssimazione low-rank mantenendo la garanzia del caso peggiore."]} +{"source": "Neural conversational models are widely used in applications like personal assistants and chat bots.These models seem to give better performance when operating on word level.However, for fusion languages like French, Russian and Polish vocabulary size sometimes become infeasible since most of the words have lots of word forms.We propose a neural network architecture for transforming normalized text into a grammatically correct one.Our model efficiently employs correspondence between normalized and target words and significantly outperforms character-level models while being 2x faster in training and 20\\% faster at evaluation.We also propose a new pipeline for building conversational models: first generate a normalized answer and then transform it into a grammatically correct one using our network.The proposed pipeline gives better performance than character-level conversational models according to assessor testing.", "target": ["Architettura proposta per risolvere il task di morphological agreement"]} +{"source": "This paper proposes the use of spectral element methods \\citep{canuto_spectral_1988} for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets; \\citealp{Chen2018NeuralOD}) for system identification.This is achieved by expressing their dynamics as a truncated series of Legendre polynomials.The series coefficients, as well as the network weights, are computed by minimizing the weighted sum of the loss function and the violation of the ODE-Net dynamics.The problem is solved by coordinate descent that alternately minimizes, with respect to the coefficients and the weights, two unconstrained sub-problems using standard backpropagation and gradient methods.The resulting optimization scheme is fully time-parallel and results in a low memory footprint.Experimental comparison to standard methods, such as backpropagation through explicit solvers and the adjoint technique \\citep{Chen2018NeuralOD}, on training surrogate models of small and medium-scale dynamical systems shows that it is at least one order of magnitude faster at reaching a comparable value of the loss function.The corresponding testing MSE is one order of magnitude smaller as well, suggesting generalization capabilities increase.", "target": ["Questo articolo propone l'uso di metodi di elementi spettrali per il training veloce e accurato delle Equazioni Differenziali Ordinarie Neurali per l'identificazione del sistema."]} +{"source": "Exploration in sparse reward reinforcement learning remains an open challenge.Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration.Commonly these signals are added as bonus rewards, which results in a mixture policy that neither conducts exploration nor task fulfillment resolutely.In this paper, we instead learn separate intrinsic and extrinsic task policies and schedule between these different drives to accelerate exploration and stabilize learning.Moreover, we introduce a new type of intrinsic reward denoted as successor feature control (SFC), which is general and not task-specific.It takes into account statistics over complete trajectories and thus differs from previous methods that only use local information to evaluate intrinsic motivation.We evaluate our proposed scheduled intrinsic drive (SID) agent using three different environments with pure visual inputs: VizDoom, DeepMind Lab and DeepMind Control Suite.The results show a substantially improved exploration efficiency with SFC and the hierarchical usage of the intrinsic drives.A video of our experimental results can be found at https://gofile.io/?c=HpEwTd.", "target": ["Un nuovo segnale di reward intrinseca basato su feature di successo e un nuovo modo di combinare la reward estrinseca e intrinseca."]} +{"source": "Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples.Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings.Existing approaches to finding efficient exploration strategies add auxiliary objectives to promote exploration by the pre-update policy, however, this makes the adaptation using a few gradient steps difficult as the pre-update (exploration) and post-update (exploitation) policies are quite different.Instead, we propose to explicitly model a separate exploration policy for the task distribution.Having two different policies gives more flexibility in training the exploration policy and also makes adaptation to any specific task easier.We show that using self-supervised or supervised learning objectives for adaptation stabilizes the training process and also demonstrate the superior performance of our model compared to prior works in this domain.", "target": ["Proponiamo di utilizzare una policy di esplorazione separata per raccogliere le traiettorie di pre-adattamento in MAML. Mostriamo anche che l'utilizzo di un obiettivo self-supervised nel ciclo interno porta ad un training più stabile e a prestazioni molto migliori."]} +{"source": "The “Supersymmetric Artificial Neural Network” in deep learning (denoted (x; θ, bar{θ})Tw), espouses the importance of considering biological constraints in the aim of further generalizing backward propagation. Looking at the progression of ‘solution geometries’; going from SO(n) representation (such as Perceptron like models) to SU(n) representation (such as UnitaryRNNs) has guaranteed richer and richer representations in weight space of the artificial neural network, and hence better and better hypotheses were generatable.The Supersymmetric Artificial Neural Network explores a natural step forward, namely SU(m|n) representation.These supersymmetric biological brain representations (Perez et al.) can be represented by supercharge compatible special unitary notation SU(m|n), or (x; θ, bar{θ})Tw parameterized by θ, bar{θ}, which are supersymmetric directions, unlike θ seen in the typical non-supersymmetric deep learning model.Notably, Supersymmetric values can encode or represent more information than the typical deep learning model, in terms of “partner potential” signals for example.", "target": ["Generalizzazione della backpropagation, usando metodi formali dalla supersimmetria."]} +{"source": "Regularization-based continual learning approaches generally prevent catastrophic forgetting by augmenting the training loss with an auxiliary objective.However in most practical optimization scenarios with noisy data and/or gradients, it is possible that stochastic gradient descent can inadvertently change critical parameters.In this paper, we argue for the importance of regularizing optimization trajectories directly.We derive a new co-natural gradient update rule for continual learning whereby the new task gradients are preconditioned with the empirical Fisher information of previously learnt tasks.We show that using the co-natural gradient systematically reduces forgetting in continual learning.Moreover, it helps combat overfitting when learning a new task in a low resource scenario.", "target": ["Regolarizzare la traiettoria di ottimizzazione con le informazioni di Fisher dei vecchi task riduce notevolmente la catastrophic forgetting"]} +{"source": "We study the problem of generating source code in a strongly typed,Java-like programming language, given a label (for example a set ofAPI calls or types) carrying a small amount of information about thecode that is desired.The generated programs are expected to respect a`\"realistic\" relationship between programs and labels, as exemplifiedby a corpus of labeled programs available during training.Two challenges in such *conditional program generation* are thatthe generated programs must satisfy a rich set of syntactic andsemantic constraints, and that source code contains many low-levelfeatures that impede learning. We address these problems by traininga neural generator not on code but on *program sketches*, ormodels of program syntax that abstract out names and operations thatdo not generalize across programs.During generation, we infer aposterior distribution over sketches, then concretize samples fromthis distribution into type-safe programs using combinatorialtechniques. We implement our ideas in a system for generatingAPI-heavy Java code, and show that it can often predict the entirebody of a method given just a few API calls or data types that appearin the method.", "target": ["Diamo un metodo per generare programmi type-safe in un linguaggio simile a Java, data una piccola quantità di informazioni sintattiche sul codice desiderato."]} +{"source": "We propose an approach for sequence modeling based on autoregressive normalizing flows.Each autoregressive transform, acting across time, serves as a moving reference frame for modeling higher-level dynamics.This technique provides a simple, general-purpose method for improving sequence modeling, with connections to existing and classical techniques.We demonstrate the proposed approach both with standalone models, as well as a part of larger sequential latent variable models.Results are presented on three benchmark video datasets, where flow-based dynamics improve log-likelihood performance over baseline models.", "target": ["Mostriamo come i flussi autoregressivi possono essere utilizzati per migliorare i modelli di variabili latenti sequenziali."]} +{"source": "It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs.This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks.We discuss a range of copyright detection systems, and why they are particularly vulnerable to attacks. These vulnerabilities are especially apparent for neural network based systems. As proof of concept, we describe a well-known music identification method and implement this system in the form of a neural net.We then attack this system using simple gradient methods.Adversarial music created this way successfully fools industrial systems, including the AudioTag copyright detector and YouTube's Content ID system.Our goal is to raise awareness of the threats posed by adversarial examples in this space and to highlight the importance of hardening copyright detection systems to attacks.", "target": ["Gli adversarial example possono ingannare il sistema di rilevamento del copyright di YouTube"]} +{"source": "Equilibrium Propagation (EP) is a learning algorithm that bridges Machine Learning and Neuroscience, by computing gradients closely matching those of Backpropagation Through Time (BPTT), but with a learning rule local in space.Given an input x and associated target y, EP proceeds in two phases: in the first phase neurons evolve freely towards a first steady state; in the second phase output neurons are nudged towards y until they reach a second steady state.However, in existing implementations of EP, the learning rule is not local in time:the weight update is performed after the dynamics of the second phase have converged and requires information of the first phase that is no longer available physically.This is a major impediment to the biological plausibility of EP and its efficient hardware implementation.In this work, we propose a version of EP named Continual Equilibrium Propagation (C-EP) where neuron and synapse dynamics occur simultaneously throughout the second phase, so that the weight update becomes local in time.We prove theoretically that, provided the learning rates are sufficiently small, at each time step of the second phase the dynamics of neurons and synapses follow the gradients of the loss given by BPTT (Theorem 1).We demonstrate training with C-EP on MNIST and generalize C-EP to neural networks where neurons are connected by asymmetric connections.We show through experiments that the more the network updates follows the gradients of BPTT, the best it performs in terms of training.These results bring EP a step closer to biology while maintaining its intimate link with backpropagation.", "target": ["Proponiamo una versione continua della Propagazione dell'Equilibrio, in cui la dinamica dei neuroni e delle sinapsi avviene simultaneamente durante la seconda fase, con garanzie teoriche e simulazioni numeriche."]} +{"source": "There are two main lines of research on visual reasoning: neural module network (NMN) with explicit multi-hop reasoning through handcrafted neural modules, and monolithic network with implicit reasoning in the latent feature space.The former excels in interpretability and compositionality, while the latter usually achieves better performance due to model flexibility and parameter efficiency. In order to bridge the gap of the two, we present Meta Module Network (MMN), a novel hybrid approach that can efficiently utilize a Meta Module to perform versatile functionalities, while preserving compositionality and interpretability through modularized design.The proposed model first parses an input question into a functional program through a Program Generator.Instead of handcrafting a task-specific network to represent each function like traditional NMN, we use Recipe Encoder to translate the functions into their corresponding recipes (specifications), which are used to dynamically instantiate the Meta Module into Instance Modules.To endow different instance modules with designated functionality, a Teacher-Student framework is proposed, where a symbolic teacher pre-executes against the scene graphs to provide guidelines for the instantiated modules (student) to follow.In a nutshell, MMN adopts the meta module to increase its parameterization efficiency, and uses recipe encoding to improve its generalization ability over NMN.Experiments conducted on the GQA benchmark demonstrates that: (1) MMN achieves significant improvement over both NMN and monolithic network baselines; (2) MMN is able to generalize to unseen but related functions.", "target": ["Proponiamo una nuova Meta Module Network per risolvere alcune delle restrizioni della precedente Neural Module Network per ottenere forti prestazioni su dataset realistici di visual reasoning."]} +{"source": "We propose a new perspective on adversarial attacks against deep reinforcement learning agents.Our main contribution is CopyCAT, a targeted attack able to consistently lure an agent into following an outsider's policy.It is pre-computed, therefore fast inferred, and could thus be usable in a real-time scenario.We show its effectiveness on Atari 2600 games in the novel read-only setting.In the latter, the adversary cannot directly modify the agent's state -its representation of the environment- but can only attack the agent's observation -its perception of the environment.Directly modifying the agent's state would require a write-access to the agent's inner workings and we argue that this assumption is too strong in realistic settings.", "target": ["Proponiamo un nuovo attacco per prendere il pieno controllo delle policy neurali in ambienti realistici."]} +{"source": "Cold-start and efficiency issues of the Top-k recommendation are critical to large-scale recommender systems.Previous hybrid recommendation methods are effective to deal with the cold-start issues by extracting real latent factors of cold-start items(users) from side information, but they still suffer low efficiency in online recommendation caused by the expensive similarity search in real latent space.This paper presents a collaborative generated hashing (CGH) to improve the efficiency by denoting users and items as binary codes, which applies to various settings: cold-start users, cold-start items and warm-start ones.Specifically, CGH is designed to learn hash functions of users and items through the Minimum Description Length (MDL) principle; thus, it can deal with various recommendation settings.In addition, CGH initiates a new marketing strategy through mining potential users by a generative step.To reconstruct effective users, the MDL principle is used to learn compact and informative binary codes from the content data.Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze the feasibility of the application in marketing.", "target": ["Può generare codici hash efficaci per un'efficiente raccomandazione a freddo e nel frattempo fornire una strategia di marketing fattibile."]} +{"source": "Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems.In this paper, we propose a neural framework that can learn to solve the Circuit Satisfiability problem.Our framework is built upon two fundamental contributions: a rich embedding architecture that encodes the problem structure and an end-to-end differentiable training procedure that mimics Reinforcement Learning and trains the model directly toward solving the SAT problem.The experimental results show the superior out-of-sample generalization performance of our framework compared to the recently developed NeuroSAT method.", "target": ["Proponiamo un framework neurale che può imparare a risolvere il problema di Circuit Satisfiability da istanze di circuito (non annotate)."]} +{"source": "Sequence generation models such as recurrent networks can be trained with a diverse set of learning algorithms.For example, maximum likelihood learning is simple and efficient, yet suffers from the exposure bias problem.Reinforcement learning like policy gradient addresses the problem but can have prohibitively poor exploration efficiency.A variety of other algorithms such as RAML, SPG, and data noising, have also been developed in different perspectives.This paper establishes a formal connection between these algorithms.We present a generalized entropy regularized policy optimization formulation, and show that the apparently divergent algorithms can all be reformulated as special instances of the framework, with the only difference being the configurations of reward function and a couple of hyperparameters.The unified interpretation offers a systematic view of the varying properties of exploration and learning efficiency.Besides, based on the framework, we present a new algorithm that dynamically interpolates among the existing algorithms for improved learning.Experiments on machine translation and text summarization demonstrate the superiority of the proposed algorithm.", "target": ["Una prospettiva unificata di vari algoritmi di apprendimento per la generazione di sequenze, come MLE, RL, RAML, data noising, ecc."]} +{"source": "We are reporting the SHINRA project, a project for structuring Wikipedia with collaborative construction scheme.The goal of the project is to create a huge and well-structured knowledge base to be used in NLP applications, such as QA, Dialogue systems and explainable NLP systems.It is created based on a scheme of ”Resource by Collaborative Contribution (RbCC)”.We conducted a shared task of structuring Wikipedia, and at the same, submitted results are used to construct a knowledge base.There are machine readable knowledge bases such as CYC, DBpedia, YAGO, Freebase Wikidata and so on, but each of them has problems to be solved.CYC has a coverage problem, and others have a coherence problem due to the fact that these are based on Wikipedia and/or created by many but inherently incoherent crowd workers.In order to solve the later problem, we started a project for structuring Wikipedia using automatic knowledge base construction shared-task.The automatic knowledge base construction shared-tasks have been popular and well studied for decades.However, these tasks are designed only to compare the performances of different systems, and to find which system ranks the best on limited test data.The results of the participated systems are not shared and the systems may be abandoned once the task is over.We believe this situation can be improved by the following changes:1. designing the shared-task to construct knowledge base rather than evaluating only limited test data2. making the outputs of all the systems open to public so that we can run ensemble learning to create the better results than the best systems3. repeating the task so that we can run the task with the larger and better training data from the output of the previous task (bootstrapping and active learning)We conducted “SHINRA2018” with the above mentioned scheme and in this paperwe report the results and the future directions of the project.The task is to extract the values of the pre-defined attributes from Wikipedia pages.We have categorized most of the entities in Japanese Wikipedia (namely 730 thousand entities) into the 200 ENE categories.Based on this data, the shared-task is to extract the values of the attributes from Wikipedia pages.We gave out the 600 training data and the participants are required to submit the attribute-values for all remaining entities of the same category type.Then 100 data out of them for each category are used to evaluate the system output in the shared-task.We conducted a preliminary ensemble learning on the outputs and found 15 F1 score improvement on a category and the average of 8 F1 score improvements on all 5 categories we tested over a strong baseline.Based on this promising results, we decided to conduct three tasks in 2019; multi-lingual categorization task (ML), extraction for the same 5 categories in Japanese with a larger training data (JP-5) and extraction for 34 new categories in Japanese (JP-34).", "target": ["Introduciamo uno schema \"Resource by Collaborative Construction\" per creare KB, Wikipedia strutturata"]} +{"source": "Recent image super-resolution(SR) studies leverage very deep convolutional neural networks and the rich hierarchical features they offered, which leads to better reconstruction performance than conventional methods.However, the small receptive fields in the up-sampling and reconstruction process of those models stop them to take full advantage of global contextual information.This causes problems for further performance improvement.In this paper, inspired by image reconstruction principles of human visual system, we propose an image super-resolution global reasoning network (SRGRN) to effectively learn the correlations between different regions of an image, through global reasoning.Specifically, we propose global reasoning up-sampling module (GRUM) and global reasoning reconstruction block (GRRB).They construct a graph model to perform relation reasoning on regions of low resolution (LR) images.They aim to reason the interactions between different regions in the up-sampling and reconstruction process and thus leverage more contextual information to generate accurate details.Our proposed SRGRN are more robust and can handle low resolution images that are corrupted by multiple types of degradation.Extensive experiments on different benchmark data-sets show that our model outperforms other state-of-the-art methods.Also our model is lightweight and consumes less computing power, which makes it very suitable for real life deployment.", "target": ["Un modello state-of-the-art basato sul ragionamento globale per la super-risoluzione delle immagini"]} +{"source": "Graph Neural Networks (GNNs) have received tremendous attention recently due to their power in handling graph data for different downstream tasks across different application domains.The key of GNN is its graph convolutional filters, and recently various kinds of filters are designed.However, there still lacks in-depth analysis on (1) Whether there exists a best filter that can perform best on all graph data; (2) Which graph properties will influence the optimal choice of graph filter; (3) How to design appropriate filter adaptive to the graph data.In this paper, we focus on addressing the above three questions.We first propose a novel assessment tool to evaluate the effectiveness of graph convolutional filters for a given graph.Using the assessment tool, we find out that there is no single filter as a `silver bullet' that perform the best on all possible graphs.In addition, different graph structure properties will influence the optimal graph convolutional filter's design choice.Based on these findings, we develop Adaptive Filter Graph Neural Network (AFGNN), a simple but powerful model that can adaptively learn task-specific filter.For a given graph, it leverages graph filter assessment as regularization and learns to combine from a set of base filters.Experiments on both synthetic and real-world benchmark datasets demonstrate that our proposed model can indeed learn an appropriate filter and perform well on graph tasks.", "target": ["Proporre un framework di valutazione per analizzare e imparare il filtro convoluzionale a grafo"]} +{"source": "The advance of node pooling operations in Graph Neural Networks (GNNs) has lagged behind the feverish design of new message-passing techniques, and pooling remains an important and challenging endeavor for the design of deep architectures.In this paper, we propose a pooling operation for GNNs that leverages a differentiable unsupervised loss based on the minCut optimization objective.For each node, our method learns a soft cluster assignment vector that depends on the node features, the target inference task (e.g., a graph classification loss), and, thanks to the minCut objective, also on the connectivity structure of the graph.Graph pooling is obtained by applying the matrix of assignment vectors to the adjacency matrix and the node features.We validate the effectiveness of the proposed pooling method on a variety of supervised and unsupervised tasks.", "target": ["Un nuovo pooling layer per GNN che impara come raggruppare i nodi, secondo le loro feature, la connettività del grafo e l'obiettivo del downstream task."]} +{"source": "Knowledge graphs are structured representations of real world facts.However, they typically contain only a small subset of all possible facts.Link prediction is the task of inferring missing facts based on existing ones.We propose TuckER, a relatively simple yet powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples.By using this particular decomposition, parameters are shared between relations, enabling multi-task learning.TuckER outperforms previous state-of-the-art models across several standard link prediction datasets.", "target": ["Proponiamo TuckER, un modello lineare relativamente semplice ma potente per la predizione dei link nei knowledge graph, basato sulla decomposizione Tucker della rappresentazione tensoriale binaria delle triple dei knowledge graph."]} +{"source": "With innovations in architecture design, deeper and wider neural network models deliver improved performance on a diverse variety of tasks.But the increased memory footprint of these models presents a challenge during training, when all intermediate layer activations need to be stored for back-propagation.Limited GPU memory forces practitioners to make sub-optimal choices: either train inefficiently with smaller batches of examples; or limit the architecture to have lower depth and width, and fewer layers at higher spatial resolutions.This work introduces an approximation strategy that significantly reduces a network's memory footprint during training, but has negligible effect on training performance and computational expense.During the forward pass, we replace activations with lower-precision approximations immediately after they have been used by subsequent layers, thus freeing up memory.The approximate activations are then used during the backward pass.This approach limits the accumulation of errors across the forward and backward pass---because the forward computation across the network still happens at full precision, and the approximation has a limited effect when computing gradients to a layer's input.Experiments, on CIFAR and ImageNet, show that using our approach with 8- and even 4-bit fixed-point approximations of 32-bit floating-point activations has only a minor effect on training and validation performance, while affording significant savings in memory usage.", "target": ["Un algoritmo per ridurre la quantità di memoria richiesta per il training delle deep network, basato su una strategia di approssimazione."]} +{"source": "Estimating the frequencies of elements in a data stream is a fundamental task in data analysis and machine learning.The problem is typically addressed using streaming algorithms which can process very large data using limited storage.Today's streaming algorithms, however, cannot exploit patterns in their input to improve performance.We propose a new class of algorithms that automatically learn relevant patterns in the input data and use them to improve its frequency estimates. The proposed algorithms combine the benefits of machine learning with the formal guarantees available through algorithm theory. We prove that our learning-based algorithms have lower estimation errors than their non-learning counterparts. We also evaluate our algorithms on two real-world datasets and demonstrate empirically their performance gains.", "target": ["Gli algoritmi di data stream possono essere migliorati utilizzando deep learning, pur mantenendo le garanzie di prestazioni."]} +{"source": "Link prediction in simple graphs is a fundamental problem in which new links between nodes are predicted based on the observed structure of the graph.However, in many real-world applications, there is a need to model relationships among nodes which go beyond pairwise associations.For example, in a chemical reaction, relationship among the reactants and products is inherently higher-order.Additionally, there is need to represent the direction from reactants to products.Hypergraphs provide a natural way to represent such complex higher-order relationships.Even though Graph Convolutional Networks (GCN) have recently emerged as a powerful deep learning-based approach for link prediction over simple graphs, their suitability for link prediction in hypergraphs is unexplored -- we fill this gap in this paper and propose Neural Hyperlink Predictor (NHP). NHP adapts GCNs for link prediction in hypergraphs. We propose two variants of NHP --NHP-U and NHP-D -- for link prediction over undirected and directed hypergraphs, respectively.To the best of our knowledge, NHP-D is the first method for link prediction over directed hypergraphs.Through extensive experiments on multiple real-world datasets, we show NHP's effectiveness.", "target": ["Noi proponiamo Neural Hyperlink Predictor (NHP). NHP adatta le graph convolutional neural network per la predizione dei link negli ipergrafi"]} +{"source": "In this paper, we present a method for adversarial decomposition of text representation.This method can be used to decompose a representation of an input sentence into several independent vectors, where each vector is responsible for a specific aspect of the input sentence.We evaluate the proposed method on two case studies: the conversion between different social registers and diachronic language change.We show that the proposed method is capable of fine-grained con- trolled change of these aspects of the input sentence.For example, our model is capable of learning a continuous (rather than categorical) representation of the style of the sentence, in line with the reality of language use.The model uses adversarial-motivational training and includes a special motivational loss, which acts opposite to the discriminator and encourages a better decomposition.Finally, we evaluate the obtained meaning embeddings on a downstream task of para- phrase detection and show that they are significantly better than embeddings of a regular autoencoder.", "target": ["Un metodo che impara rappresentazioni separate per il significato e la forma di una frase"]} +{"source": "We were approached by a group of healthcare providers who are involved in the care of chronic patients looking for potential technologies to facilitate the process of reviewing patient-generated data during clinical visits. Aiming at understanding the healthcare providers' attitudes towards reviewing patient-generated data, we (1) conducted a focus group with a mixed group of healthcare providers.Next, to gain the patients' perspectives, we (2) interviewed eight chronic patients, collected a sample of their data and designed a series of visualizations representing patient data we collected.Last, we (3) sought feedback on the visualization designs from healthcare providers who requested this exploration.We found four factors shaping patient-generated data: data & context, patient's motivation, patient's time commitment, and patient's support circle.Informed by the results of our studies, we discussed the importance of designing patient-generated visualizations for individuals by considering both patient and healthcare provider rather than designing with the purpose of generalization and provided guidelines for designing future patient-generated data visualizations.", "target": ["Abbiamo esplorato i disegn di visualizzazione che possono sostenere i pazienti cronici per presentare e rivedere i loro dati sanitari con i fornitori di assistenza sanitaria durante le visite cliniche."]} +{"source": "Recently, neural-network based forward dynamics models have been proposed that attempt to learn the dynamics of physical systems in a deterministic way.While near-term motion can be predicted accurately, long-term predictions suffer from accumulating input and prediction errors which can lead to plausible but different trajectories that diverge from the ground truth. A system that predicts distributions of the future physical states for long time horizons based on its uncertainty is thus a promising solution. In this work, we introduce a novel robust Monte Carlo sampling based graph-convolutional dropout method that allows us to sample multiple plausible trajectories for an initial state given a neural-network based forward dynamics predictor. By introducing a new shape preservation loss and training our dynamics model recurrently, we stabilize long-term predictions.We show that our model’s long-term forward dynamics prediction errors on complicated physical interactions of rigid and deformable objects of various shapes are significantly lower than existing strong baselines.Lastly, we demonstrate how generating multiple trajectories with our Monte Carlo dropout method can be used to train model-free reinforcement learning agents faster and to better solutions on simple manipulation tasks.", "target": ["Proponiamo un predittore stocastico di dinamiche forward differenziabili che è in grado di campionare più traiettorie fisicamente plausibili sotto lo stesso stato iniziale di input e mostriamo che può essere utilizzato per addestrare policy senza modello in modo più efficiente."]} +{"source": "There has been a large amount of interest, both in the past and particularly recently, into the relative advantage of different families of universal function approximators, for instance neural networks, polynomials, rational functions, etc.However, current research has focused almost exclusively on understanding this problem in a worst case setting: e.g. characterizing the best L1 or L_{infty} approximation in a box (or sometimes, even under an adversarially constructed data distribution.)In this setting many classical tools from approximation theory can be effectively used.However, in typical applications we expect data to be high dimensional, but structured -- so, it would only be important to approximate the desired function well on the relevant part of its domain, e.g. a small manifold on which real input data actually lies.Moreover, even within this domain the desired quality of approximation may not be uniform; for instance in classification problems, the approximation needs to be more accurate near the decision boundary.These issues, to the best of our knowledge, have remain unexplored until now.\tWith this in mind, we analyze the performance of neural networks and polynomial kernels in a natural regression setting where the data enjoys sparse latent structure, and the labels depend in a simple way on the latent variables.We give an almost-tight theoretical analysis of the performance of both neural networks and polynomials for this problem, as well as verify our theory with simulations.Our results both involve new (complex-analytic) techniques, which may be of independent interest, and show substantial qualitative differences with what is known in the worst-case setting.", "target": ["Analisi del caso peggiore del potere di rappresentazione delle reti ReLU e dei kernel polinomiali - in particolare in presenza di strutture latenti sparse."]} +{"source": "Recent deep generative models can provide photo-realistic images as well as visual or textual content embeddings useful to address various tasks of computer vision and natural language processing.Their usefulness is nevertheless often limited by the lack of control over the generative process or the poor understanding of the learned representation.To overcome these major issues, very recent works have shown the interest of studying the semantics of the latent space of generative models.In this paper, we propose to advance on the interpretability of the latent space of generative models by introducing a new method to find meaningful directions in the latent space of any generative model along which we can move to control precisely specific properties of the generated image like position or scale of the object in the image.Our method is weakly supervised and particularly well suited for the search of directions encoding simple transformations of the generated image, such as translation, zoom or color variations.We demonstrate the effectiveness of our method qualitatively and quantitatively, both for GANs and variational auto-encoders.", "target": ["Un modello per controllare la generazione di immagini con GAN e beta-VAE per quanto riguarda la scala e la posizione degli oggetti"]} +{"source": "Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps.However, choosing which of the myriad structured transformations to use (and its associated parameterization) is a laborious task that requires trading off speed, space, and accuracy.We consider a different approach: we introduce a family of matrices called kaleidoscope matrices (K-matrices) that provably capture any structured matrix with near-optimal space (parameter) and time (arithmetic operation) complexity.We empirically validate that K-matrices can be automatically learned within end-to-end pipelines to replace hand-crafted procedures, in order to improve model quality.For example, replacing channel shuffles in ShuffleNet improves classification accuracy on ImageNet by up to 5%.Learnable K-matrices can also simplify hand-engineered pipelines---we replace filter bank feature computation in speech data preprocessing with a kaleidoscope layer, resulting in only 0.4% loss in accuracy on the TIMIT speech recognition task.K-matrices can also capture latent structure in models: for a challenging permuted image classification task, adding a K-matrix to a standard convolutional architecture can enable learning the latent permutation and improve accuracy by over 8 points.We provide a practically efficient implementation of our approach, and use K-matrices in a Transformer network to attain 36% faster end-to-end inference speed on a language translation task.", "target": ["Proponiamo una famiglia differenziabile di \"matrici caleidoscopio\", dimostriamo che tutte le matrici strutturate possono essere rappresentate in questa forma, e le usiamo per sostituire le mappe lineari hand-crafted nei modelli di deep learning."]} +{"source": "We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks.With the proposed method, the label embedding is adaptively and automatically learned through back propagation.The original one-hot represented loss function is converted into a new loss function with soft distributions, such that the originally unrelated labels have continuous interactions with each other during the training process.As a result, the trained model can achieve substantially higher accuracy and with faster convergence speed.Experimental results based on competitive tasks demonstrate the effectiveness of the proposed method, and the learned label embedding is reasonable and interpretable.The proposed method achieves comparable or even better results than the state-of-the-art systems.", "target": ["Imparare la rappresentazione delle label per le deep network"]} +{"source": "Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters.As current approaches are limited by network bandwidth, we propose the use of communication compression in the decentralized training context.We show that Choco-SGD achieves linear speedup in the number of workers for arbitrary high compression ratios on general non-convex functions, and non-IID training data. We demonstrate the practical performance of the algorithm in two key scenarios: the training of deep learning models(i) over decentralized user devices, connected by a peer-to-peer network and(ii) in a datacenter.", "target": ["Proponiamo Choco-SGD - SGD decentralizzato con comunicazione compressa - per obiettivi non convessi e mostriamo le sue forti prestazioni in varie applicazioni di deep learning (apprendimento on-device, o in datacenter)."]} +{"source": "We show that Entropy-SGD (Chaudhari et al., 2017), when viewed as a learning algorithm, optimizes a PAC-Bayes bound on the risk of a Gibbs (posterior) classifier, i.e., a randomized classifier obtained by a risk-sensitive perturbation of the weights of a learned classifier.Entropy-SGD works by optimizing the bound’s prior, violating the hypothesis of the PAC-Bayes theorem that the prior is chosen independently of the data.Indeed, available implementations of Entropy-SGD rapidly obtain zero training error on random labels and the same holds of the Gibbs posterior.In order to obtain a valid generalization bound, we show that an ε-differentially private prior yields a valid PAC-Bayes bound, a straightforward consequence of results connecting generalization with differential privacy.Using stochastic gradient Langevin dynamics (SGLD) to approximate the well-known exponential release mechanism, we observe that generalization error on MNIST (measured on held out data) falls within the (empirically nonvacuous) bounds computed under the assumption that SGLD produces perfect samples.In particular, Entropy-SGLD can be configured to yield relatively tight generalization bounds and still fit real labels, although these same settings do not obtain state-of-the-art performance.", "target": ["Mostriamo che Entropy-SGD ottimizza il prior di un PAC-Bayes bound, violando il requisito che il prior sia indipendente dai dati; usiamo la privacy differenziale per risolvere questo fatto e migliorare la generalizzazione."]} +{"source": "In this paper, we investigate learning the deep neural networks for automated optical inspection in industrial manufacturing.Our preliminary result has shown the stunning performance improvement by transfer learning from the completely dissimilar source domain: ImageNet.Further study for demystifying this improvement shows that the transfer learning produces a highly compressible network, which was not the case for the network learned from scratch.The experimental result shows that there is a negligible accuracy drop in the network learned by transfer learning until it is compressed to 1/128 reduction of the number of convolution filters.This result is contrary to the compression without transfer learning which loses more than 5% accuracy at the same compression rate.", "target": ["Mostriamo sperimentalmente che il transfer learning rende le feature sparse nella rete e quindi produce una rete più comprimibile."]} +{"source": "The key challenge in semi-supervised learning is how to effectively leverage unlabeled data to improve learning performance.The classical label propagation method, despite its popularity, has limited modeling capability in that it only exploits graph information for making predictions.In this paper, we consider label propagation from a graph signal processing perspective and decompose it into three components: signal, filter, and classifier.By extending the three components, we propose a simple generalized label propagation (GLP) framework for semi-supervised learning.GLP naturally integrates graph and data feature information, and offers the flexibility of selecting appropriate filters and domain-specific classifiers for different applications.Interestingly, GLP also provides new insight into the popular graph convolutional network and elucidates its working mechanisms.Extensive experiments on three citation networks, one knowledge graph, and one image dataset demonstrate the efficiency and effectiveness of GLP.", "target": ["Estendiamo i metodi classici di label propagation per modellare congiuntamente le informazioni del grafo e delle feature da una prospettiva di filtraggio del grafo, e mostriamo le connessioni con le graph convolutional network."]} +{"source": "Because the choice and tuning of the optimizer affects the speed, and ultimately the performance of deep learning, there is significant past and recent research in this area.Yet, perhaps surprisingly, there is no generally agreed-upon protocol for the quantitative and reproducible evaluation of optimization strategies for deep learning.We suggest routines and benchmarks for stochastic optimization, with special focus on the unique aspects of deep learning, such as stochasticity, tunability and generalization.As the primary contribution, we present DeepOBS, a Python package of deep learning optimization benchmarks.The package addresses key challenges in the quantitative assessment of stochastic optimizers, and automates most steps of benchmarking.The library includes a wide and extensible set of ready-to-use realistic optimization problems, such as training Residual Networks for image classification on ImageNet or character-level language prediction models, as well as popular classics like MNIST and CIFAR-10.The package also provides realistic baseline results for the most popular optimizers on these test problems, ensuring a fair comparison to the competition when benchmarking new optimizers, and without having to run costly experiments.It comes with output back-ends that directly produce LaTeX code for inclusion in academic publications.It supports TensorFlow and is available open source.", "target": ["Forniamo un pacchetto software che drasticamente semplifica, automatizza e migliora la valutazione degli ottimizzatori di deep learning."]} +{"source": "Pre-trained word embeddings are the primarymethod for transfer learning in several Natural Language Processing (NLP) tasks.Recentworks have focused on using unsupervisedtechniques such as language modeling to obtain these embeddings.In contrast, this workfocuses on extracting representations frommultiple pre-trained supervised models, whichenriches word embeddings with task and domain specific knowledge.Experiments performed in cross-task, cross-domain and crosslingual settings indicate that such supervisedembeddings are helpful, especially in the lowresource setting, but the extent of gains is dependent on the nature of the task and domain.", "target": ["Estrarre embedding contestuali da un modello supervised off-the-shelf. Aiuta i modelli NLP downstream in ambienti low-resource."]} +{"source": "We build a theoretical framework for understanding practical meta-learning methods that enables the integration of sophisticated formalizations of task-similarity with the extensive literature on online convex optimization and sequential prediction algorithms in order to provide within-task performance guarantees.Our approach improves upon recent analyses of parameter-transfer by enabling the task-similarity to be learned adaptively and by improving transfer-risk bounds in the setting of statistical learning-to-learn.It also leads to straightforward derivations of average-case regret bounds for efficient algorithms in settings where the task-environment changes dynamically or the tasks share a certain geometric structure.", "target": ["Algoritmi adattivi pratici per il meta-learning basato sul gradiente con garanzie dimostrabili."]} +{"source": "In this work, we propose a self-supervised method to learn sentence representations with an injection of linguistic knowledge.Multiple linguistic frameworks propose diverse sentence structures from which semantic meaning might be expressed out of compositional words operations.We aim to take advantage of this linguist diversity and learn to represent sentences by contrasting these diverse views.Formally, multiple views of the same sentence are mapped to close representations.On the contrary, views from other sentences are mapped further.By contrasting different linguistic views, we aim at building embeddings which better capture semantic and which are less sensitive to the sentence outward form.", "target": ["Miriamo a sfruttare la diversità delle strutture linguistiche per costruire rappresentazioni di frasi."]} +{"source": "The peripheral nervous system represents the input/output system for the brain.Cuff electrodes implanted on the peripheral nervous system allow observation and control over this system, however, the data produced by these electrodes have a low signal-to-noise ratio and a complex signal content.In this paper, we consider the analysis of neural data recorded from the vagus nerve in animal models, and develop an unsupervised learner based on convolutional neural networks that is able to simultaneously de-noise and cluster regions of the data by signal content.", "target": ["L'analisi non supervisionata dei dati registrati dal sistema nervoso periferico categorizza i segnali e ne riduce il rumore."]} +{"source": "Adversarial attacks on convolutional neural networks (CNN) have gained significant attention and there have been active research efforts on defense mechanisms.Stochastic input transformation methods have been proposed, where the idea is to recover the image from adversarial attack by random transformation, and to take the majority vote as consensus among the random samples.However, the transformation improves the accuracy on adversarial images at the expense of the accuracy on clean images.While it is intuitive that the accuracy on clean images would deteriorate, the exact mechanism in which how this occurs is unclear.In this paper, we study the distribution of softmax induced by stochastic transformations.We observe that with random transformations on the clean images, although the mass of the softmax distribution could shift to the wrong class, the resulting distribution of softmax could be used to correct the prediction.Furthermore, on the adversarial counterparts, with the image transformation, the resulting shapes of the distribution of softmax are similar to the distributions from the clean images.With these observations, we propose a method to improve existing transformation-based defenses.We train a separate lightweight distribution classifier to recognize distinct features in the distributions of softmax outputs of transformed images.Our empirical studies show that our distribution classifier, by training on distributions obtained from clean images only, outperforms majority voting for both clean and adversarial images.Our method is generic and can be integrated with existing transformation-based defenses.", "target": ["Miglioriamo le difese esistenti basate sulla trasformazione utilizzando un classificatore di distribuzione sulla distribuzione di softmax ottenuta dalle immagini trasformate."]} +{"source": "Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings.We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep.This attention mechanism enables more effective and scalable learning in complex multi-agent environments, when compared to recent approaches.Our approach is applicable not only to cooperative settings with shared rewards, but also individualized reward settings, including adversarial settings, and it makes no assumptions about the action spaces of the agents.As such, it is flexible enough to be applied to most multi-agent learning problems", "target": ["Proponiamo un approccio per apprendere policy decentralizzate in ambienti multi-agente usando criteri basati sull'attention e dimostriamo risultati promettenti in ambienti con interazioni complesse."]} +{"source": "The recent expansion of machine learning applications to molecular biology proved to have a significant contribution to our understanding of biological systems, and genome functioning in particular.Technological advances enabled the collection of large epigenetic datasets, including information about various DNA binding factors (ChIP-Seq) and DNA spatial structure (Hi-C).Several studies have confirmed the correlation between DNA binding factors and Topologically Associating Domains (TADs) in DNA structure.However, the information about physical proximity represented by genomic coordinate was not yet used for the improvement of the prediction models.In this research, we focus on Machine Learning methods for prediction of folding patterns of DNA in a classical model organism Drosophila melanogaster.The paper considers linear models with four types of regularization, Gradient Boosting and Recurrent Neural Networks for the prediction of chromatin folding patterns from epigenetic marks.The bidirectional LSTM RNN model outperformed all the models and gained the best prediction scores.This demonstrates the utilization of complex models and the importance of memory of sequential DNA states for the chromatin folding.We identify informative epigenetic features that lead to the further conclusion of their biological significance.", "target": ["Applichiamo RNN per risolvere il problema biologico della predizione dei modelli di ripiegamento della cromatina dai segni epigenetici e dimostriamo per la prima volta che l'utilizzo della memoria degli stati sequenziali sulla molecola di DNA è significativo per la migliore performance."]} +{"source": "Previous work showed empirically that large neural networks can be significantly reduced in size while preserving their accuracy.Model compression became a central research topic, as it is crucial for deployment of neural networks on devices with limited computational and memory resources.The majority of the compression methods are based on heuristics and offer no worst-case guarantees on the trade-off between the compression rate and the approximation error for an arbitrarily new sample.We propose the first efficient, data-independent neural pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample.Our method is based on the coreset framework, which finds a small weighted subset of points that provably approximates the original inputs.Specifically, we approximate the output of a layer of neurons by a coreset of neurons in the previous layer and discard the rest.We apply this framework in a layer-by-layer fashion from the top to the bottom.Unlike previous works, our coreset is data independent, meaning that it provably guarantees the accuracy of the function for any input $x\\in \\mathbb{R}^d$, including an adversarial one.We demonstrate the effectiveness of our method on popular network architectures.In particular, our coresets yield 90% compression of the LeNet-300-100 architecture on MNIST while improving the accuracy.", "target": ["Proponiamo un metodo efficiente, dimostrabile e indipendente dai dati per la compressione della rete attraverso il pruning neurale usando i coreset di neuroni - una nuova costruzione proposta in questo articolo."]} +{"source": "Planning problems in partially observable environments cannot be solved directly with convolutional networks and require some form of memory.But, even memory networks with sophisticated addressing schemes are unable to learn intelligent reasoning satisfactorily due to the complexity of simultaneously learning to access memory and plan.To mitigate these challenges we propose the Memory Augmented Control Network (MACN).The network splits planning into a hierarchical process.At a lower level, it learns to plan in a locally observed space.At a higher level, it uses a collection of policies computed on locally observed spaces to learn an optimal plan in the global environment it is operating in.The performance of the network is evaluated on path planning tasks in environments in the presence of simple and complex obstacles and in addition, is tested for its ability to generalize to new environments not seen in the training set.", "target": ["Memory Augmented Network per pianificare in ambienti parzialmente osservabili."]} +{"source": "Contextualized word representations such as ELMo and BERT have become the de facto starting point for incorporating pretrained representations for downstream NLP tasks.In these settings, contextual representations have largely made obsolete their static embedding predecessors such as Word2Vec and GloVe.However, static embeddings do have their advantages in that they are straightforward to understand and faster to use.Additionally, embedding analysis methods for static embeddings are far more diverse and mature than those available for their dynamic counterparts.In this work, we introduce simple methods for generating static lookup table embeddings from existing pretrained contextual representations and demonstrate they outperform Word2Vec and GloVe embeddings on a variety of word similarity and word relatedness tasks.In doing so, our results also reveal insights that may be useful for subsequent downstream tasks using our embeddings or the original contextual models.Further, we demonstrate the increased potential for analysis by applying existing approaches for estimating social bias in word embeddings.Our analysis constitutes the most comprehensive study of social bias in contextual word representations (via the proxy of our distilled embeddings) and reveals a number of inconsistencies in current techniques for quantifying social bias in word embeddings.We publicly release our code and distilled word embeddings to support reproducible research and the broader NLP community.", "target": ["Una procedura per distillare i modelli contestuali in embedding statici; applichiamo il nostro metodo a 9 modelli popolari e dimostriamo chiari guadagni in qualità della rappresentazione rispetto a Word2Vec/GloVe e un potenziale di analisi migliorato studiando a fondo i bias sociali."]} +{"source": "The brain performs unsupervised learning and (perhaps) simultaneous supervised learning.This raises the question as to whether a hybrid of supervised and unsupervised methods will produce better learning.Inspired by the rich space of Hebbian learning rules, we set out to directly learn the unsupervised learning rule on local information that best augments a supervised signal.We present the Hebbian-augmented training algorithm (HAT) for combining gradient-based learning with an unsupervised rule on pre-synpatic activity, post-synaptic activities, and current weights.We test HAT's effect on a simple problem (Fashion-MNIST) and find consistently higher performance than supervised learning alone.This finding provides empirical evidence that unsupervised learning on synaptic activities provides a strong signal that can be used to augment gradient-based methods. We further find that the meta-learned update rule is a time-varying function; thus, it is difficult to pinpoint an interpretable Hebbian update rule that aids in training. We do find that the meta-learner eventually degenerates into a non-Hebbian rule that preserves important weights so as not to disturb the learner's convergence.", "target": ["Un update rule con metalearning unsupervised per le reti neurali migliora le prestazioni e dimostra potenzialmente come i neuroni nel cervello imparino senza accesso alle label globali."]} +{"source": "Deep convolutional networks often append additive constant (\"bias\") terms to their convolution operations, enabling a richer repertoire of functional mappings.Biases are also used to facilitate training, by subtracting mean response over batches of training images (a component of \"batch normalization\").Recent state-of-the-art blind denoising methods seem to require these terms for their success.Here, however, we show that bias terms used in most CNNs (additive constants, including those used for batch normalization) interfere with the interpretability of these networks, do not help performance, and in fact prevent generalization of performance to noise levels not including in the training data.In particular, bias-free CNNs (BF-CNNs) are locally linear, and hence amenable to direct analysis with linear-algebraic tools.These analyses provide interpretations of network functionality in terms of projection onto a union of low-dimensional subspaces, connecting the learning-based method to more traditional denoising methodology.Additionally, BF-CNNs generalize robustly, achieving near-state-of-the-art performance at noise levels well beyond the range over which they have been trained.", "target": ["Mostriamo che la rimozione dei termini costanti dalle architetture CNN fornisce l'interpretabilità del metodo di denoising attraverso tecniche di algebra lineare e aumenta anche le prestazioni di generalizzazione attraverso i livelli di rumore."]} +{"source": "The selection of initial parameter values for gradient-based optimization of deep neural networks is one of the most impactful hyperparameter choices in deep learning systems, affecting both convergence times and model performance.Yet despite significant empirical and theoretical analysis, relatively little has been proved about the concrete effects of different initialization schemes.In this work, we analyze the effect of initialization in deep linear networks, and provide for the first time a rigorous proof that drawing the initial weights from the orthogonal group speeds up convergence relative to the standard Gaussian initialization with iid weights.We show that for deep networks, the width needed for efficient convergence to a global minimum with orthogonal initializations is independent of the depth, whereas the width needed for efficient convergence with Gaussian initializations scales linearly in the depth.Our results demonstrate how the benefits of a good initialization can persist throughout learning, suggesting an explanation for the recent empirical successes found by initializing very deep non-linear networks according to the principle of dynamical isometry.", "target": ["Forniamo per la prima volta una prova rigorosa che l'inizializzazione ortogonale accelera la convergenza rispetto all'inizializzazione gaussiana, per deep linear network."]} +{"source": "Survival function estimation is used in many disciplines, but it is most common in medical analytics in the form of the Kaplan-Meier estimator.Sensitive data (patient records) is used in the estimation without any explicit control on the information leakage, which is a significant privacy concern.We propose a first differentially private estimator of the survival function and show that it can be easily extended to provide differentially private confidence intervals and test statistics without spending any extra privacy budget.We further provide extensions for differentially private estimation of the competing risk cumulative incidence function.Using nine real-life clinical datasets, we provide empirical evidence that our proposed method provides good utility while simultaneously providing strong privacy guarantees.", "target": ["Una prima stima differentially private della funzione di sopravvivenza"]} +{"source": "Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning.Although some label structure can implicitly be obtained when training on huge amounts of data, in a few-shot learning context where little data is available, making explicit use of the label structure can inform the model to reshape the representation space to reflect a global sense of class dependencies. We propose a meta-learning framework, Conditional class-Aware Meta-Learning (CAML), that conditionally transforms feature representations based on a metric space that is trained to capture inter-class dependencies.This enables a conditional modulation of the feature representations of the base-learner to impose regularities informed by the label space.Experiments show that the conditional transformation in CAML leads to more disentangled representations and achieves competitive results on the miniImageNet benchmark.", "target": ["CAML è un'istanza di MAML con dipendenze di classe condizionali."]} +{"source": "We study the problem of multiset prediction.The goal of multiset prediction is to train a predictor that maps an input to a multiset consisting of multiple items.Unlike existing problems in supervised learning, such as classification, ranking and sequence generation, there is no known order among items in a target multiset, and each item in the multiset may appear more than once, making this problem extremely challenging.In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making.The proposed multiset loss function is empirically evaluated on two families of datasets, one synthetic and the other real, with varying levels of difficulty, against various baseline loss functions including reinforcement learning, sequence, and aggregated distribution matching loss functions.The experiments reveal the effectiveness of the proposed loss function over the others.", "target": ["Studiamo il problema della predizione di multiset e proponiamo una nuova loss multiset, fornendo analisi e prove empiriche che dimostrano la sua efficacia."]} +{"source": "Understanding theoretical properties of deep and locally connected nonlinear network, such as deep convolutional neural network (DCNN), is still a hard problem despite its empirical success.In this paper, we propose a novel theoretical framework for such networks with ReLU nonlinearity.The framework bridges data distribution with gradient descent rules, favors disentangled representations and is compatible with common regularization techniques such as Batch Norm, after a novel discovery of its projection nature.The framework is built upon teacher-student setting, by projecting the student's forward/backward pass onto the teacher's computational graph.We do not impose unrealistic assumptions (e.g., Gaussian inputs, independence of activation, etc).Our framework could help facilitate theoretical analysis of many practical issues, e.g. disentangled representations in deep networks.", "target": ["Questo articolo presenta un framework teorico che modella esplicitamente la distribuzione dei dati per le reti ReLU profonde e connesse localmente"]} +{"source": "Multi-agent cooperation is an important feature of the natural world.Many tasks involve individual incentives that are misaligned with the common good, yet a wide range of organisms from bacteria to insects and humans are able to overcome their differences and collaborate.Therefore, the emergence of cooperative behavior amongst self-interested individuals is an important question for the fields of multi-agent reinforcement learning (MARL) and evolutionary theory.Here, we study a particular class of multi-agent problems called intertemporal social dilemmas (ISDs), where the conflict between the individual and the group is particularly sharp.By combining MARL with appropriately structured natural selection, we demonstrate that individual inductive biases for cooperation can be learned in a model-free way.To achieve this, we introduce an innovative modular architecture for deep reinforcement learning agents which supports multi-level selection.We present results in two challenging environments, and interpret these in the context of cultural and ecological evolution.", "target": ["Introduciamo un algoritmo evolutivo modulare biologicamente ispirato in cui gli agenti di deep RL imparano a cooperare in un difficile gioco sociale multi-agente, che potrebbe aiutare a spiegare l'evoluzione dell'altruismo."]} +{"source": "In adversarial attacks to machine-learning classifiers, small perturbations are added to input that is correctly classified.The perturbations yield adversarial examples, which are virtually indistinguishable from the unperturbed input, and yet are misclassified.In standard neural networks used for deep learning, attackers can craft adversarial examples from most input to cause a misclassification of their choice. We introduce a new type of network units, called RBFI units, whose non-linear structure makes them inherently resistant to adversarial attacks.On permutation-invariant MNIST, in absence of adversarial attacks, networks using RBFI units match the performance of networks using sigmoid units, and are slightly below the accuracy of networks with ReLU units.When subjected to adversarial attacks based on projected gradient descent or fast gradient-sign methods, networks with RBFI units retain accuracies above 75%, while ReLU or Sigmoid see their accuracies reduced to below 1%.Further, RBFI networks trained on regular input either exceed or closely match the accuracy of sigmoid and ReLU network trained with the help of adversarial examples.The non-linear structure of RBFI units makes them difficult to train using standard gradient descent.We show that RBFI networks of RBFI units can be efficiently trained to high accuracies using pseudogradients, computed using functions especially crafted to facilitate learning instead of their true derivatives.", "target": ["Introduciamo un tipo di rete neurale che è strutturalmente resistente agli adversarial attack, anche quando viene addestrata su set di allenamento non modificati. La resistenza è dovuta alla stabilità delle unità della rete rispetto alle perturbazioni di input."]} +{"source": "We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy.We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists even in a fairly simple and natural setting.These findings also corroborate a similar phenomenon observed in practice.Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers.These differences, in particular, seem to result in unexpected benefits: the features learned by robust models tend to align better with salient data characteristics and human perception.", "target": ["Mostriamo che l'adversarial robustness potrebbe diminuire le prestazioni di classificazione standard, ma produce anche benefici inaspettati."]} +{"source": "Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples.In this work, we propose mixup, a simple learning principle to alleviate these issues.In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.", "target": ["Il training su combinazioni convesse tra esempi di training casuali e le loro label migliora la generalizzazione nelle deep neural network"]} +{"source": "We present a novel approach to spike sorting for high-density multielectrode probes using the Neural Clustering Process (NCP), a recently introduced neural architecture that performs scalable amortized approximate Bayesian inference for efficient probabilistic clustering.To optimally encode spike waveforms for clustering, we extended NCP by adding a convolutional spike encoder, which is learned end-to-end with the NCP network.Trained purely on labeled synthetic spikes from a simple generative model, the NCP spike sorting model shows promising performance for clustering multi-channel spike waveforms.The model provides higher clustering quality than an alternative Bayesian algorithm, finds more spike templates with clear receptive fields on real data and recovers more ground truth neurons on hybrid test data compared to a recent spike sorting algorithm.Furthermore, NCP is able to handle the clustering uncertainty of ambiguous small spikes by GPU-parallelized posterior sampling.The source code is publicly available.", "target": ["Presentiamo un nuovo approccio allo spike sorting utilizzando il Neural Clustering Process (NCP), un'architettura neurale di recente introduzione che esegue un'inferenza bayesiana approssimativa che è ammortizzata e scalabile per un efficiente clustering probabilistico."]} +{"source": "The goal of the paper is to propose an algorithm for learning the most generalizable solution from given training data.It is shown that Bayesian approach leads to a solution that dependent on statistics of training data and not on particularsamples.The solution is stable under perturbations of training data because it is defined by an integral contribution of multiple maxima of the likelihood and not by a single global maximum.Specifically, the Bayesian probability distributionof parameters (weights) of a probabilistic model given by a neural network is estimated via recurrent variational approximations.Derived recurrent update rules correspond to SGD-type rules for finding a minimum of an effective loss that is an average of an original negative log-likelihood over the Gaussian distributions of weights, which makes it a function of means and variances.The effective loss is convex for large variances and non-convex in the limit of small variances.Among stationary solutions of the update rules there are trivial solutions with zero variances at local minima of the original loss and a single non-trivial solution with finite variances that is a critical point at the end of convexity of the effective lossin the mean-variance space.At the critical point both first- and second-order gradients of the effective loss w.r.t. means are zero.The empirical study confirms that the critical point represents the most generalizable solution.While the location ofthe critical point in the weight space depends on specifics of the used probabilistic model some properties at the critical point are universal and model independent.", "target": ["Metodo proposto per trovare la soluzione più generalizzabile che sia stabile con le perturbazioni dei dati di training."]} +{"source": "Humans rely on episodic memory constantly, in remembering the name of someone they met 10 minutes ago, the plot of a movie as it unfolds, or where they parked the car.Endowing reinforcement learning agents with episodic memory is a key step on the path toward replicating human-like general intelligence.We analyze why standard RL agents lack episodic memory today, and why existing RL tasks don't require it.We design a new form of external memory called Masked Experience Memory, or MEM, modeled after key features of human episodic memory.To evaluate episodic memory we define an RL task based on the common children's game of Concentration.We find that a MEM RL agent leverages episodic memory effectively to master Concentration, unlike the baseline agents we tested.", "target": ["Implementazione e valutazione della memoria episodica per RL."]} +{"source": "Parameters are one of the most critical components of machine learning models.As datasets and learning domains change, it is often necessary and time-consuming to re-learn entire models.Rather than re-learning the parameters from scratch, replacing learning with optimization, we propose a framework building upon the theory of \\emph{optimal transport} to adapt model parameters by discovering correspondences between models and data, significantly amortizing the training cost.We demonstrate our idea on the challenging problem of creating probabilistic spatial representations for autonomous robots.Although recent mapping techniques have facilitated robust occupancy mapping, learning all spatially-diverse parameters in such approximate Bayesian models demand considerable computational time, discouraging them to be used in real-world robotic mapping.Considering the fact that the geometric features a robot would observe with its sensors are similar across various environments, in this paper, we demonstrate how to re-use parameters and hyperparameters learned in different domains.This adaptation is computationally more efficient than variational inference and Monte Carlo techniques.A series of experiments conducted on realistic settings verified the possibility of transferring thousands of such parameters with a negligible time and memory cost, enabling large-scale mapping in urban environments.", "target": ["Presentiamo un metodo per adattare gli iperparametri dei modelli probabilistici utilizzando il trasporto ottimale con applicazioni nella robotica"]} +{"source": "An algorithm is introduced for learning a predictive state representation with off-policy temporal difference (TD) learning that is then used to learn to steer a vehicle with reinforcement learning. There are three components being learned simultaneously: (1) the off-policy predictions as a compact representation of state, (2) the behavior policy distribution for estimating the off-policy predictions, and (3) the deterministic policy gradient for learning to act. A behavior policy discriminator is learned and used for estimating the important sampling ratios needed to learn the predictive representation off-policy with general value functions (GVFs). A linear deterministic policy gradient method is used to train the agent with only the predictive representations while the predictions are being learned. All three components are combined, demonstrated and evaluated on the problem of steering the vehicle from images in the TORCS racing simulator environment.Steering from only images is a challenging problem where evaluation is completed on a held-out set of tracks that were never seen during training in order to measure the generalization of the predictions and controller. Experiments show the proposed method is able to steer smoothly and navigate many but not all of the tracks available in TORCS with performance that exceeds DDPG using only images as input and approaches the performance of an ideal non-vision based kinematics model.", "target": ["Un algoritmo per imparare una rappresentazione di stato predittiva con funzioni di valore generale e apprendimento off-policy viene applicato al problema dello steering basato sulla visione nella guida autonoma."]} +{"source": "For numerous domains, including for instance earth observation, medical imaging, astrophysics,..., available image and signal datasets often irregular space-time sampling patterns and large missing data rates.These sampling properties is a critical issue to apply state-of-the-art learning-based (e.g., auto-encoders, CNNs,...) to fully benefit from the available large-scale observations and reach breakthroughs in the reconstruction and identification of processes of interest.In this paper, we address the end-to-end learning of representations of signals, images and image sequences from irregularly-sampled data, {\\em i.e.} when the training data involved missing data.From an analogy to Bayesian formulation, we consider energy-based representations.Two energy forms are investigated: one derived from auto-encoders and one relating to Gibbs energies.The learning stage of these energy-based representations (or priors) involve a joint interpolation issue, which resorts to solving an energy minimization problem under observation constraints.Using a neural-network-based implementation of the considered energy forms, we can state an end-to-end learning scheme from irregularly-sampled data.We demonstrate the relevance of the proposed representations for different case-studies: namely, multivariate time series, 2{\\sc } images and image sequences.", "target": ["Ci occupiamo dell'apprendimento end-to-end di rappresentazioni basate sull'energia per i dataset di osservazione di segnali e immagini con modelli di campionamento irregolari."]} +{"source": "Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively.This leads to poor sample-efficiency during meta-training as well as ineffective task identification strategies.This paper provides a theoretical analysis of credit assignment in gradient-based Meta-RL.Building on the gained insights we develop a novel meta-learning algorithm that overcomes both the issue of poor credit assignment and previous difficulties in estimating meta-policy gradients.By controlling the statistical distance of both pre-adaptation and adapted policies during meta-policy search, the proposed algorithm endows efficient and stable meta-learning.Our approach leads to superior pre-adaptation policy behavior and consistently outperforms previous Meta-RL algorithms in sample-efficiency, wall-clock time, and asymptotic performance.", "target": ["Un nuovo algoritmo di meta-reinforcement learning teoricamente solido"]} +{"source": "When training a neural network for a desired task, one may prefer to adapt a pretrained network rather than start with a randomly initialized one -- due to lacking enough training data, performing lifelong learning where the system has to learn a new task while being previously trained for other tasks, or wishing to encode priors in the network via preset weights.The most commonly employed approaches for network adaptation are fine-tuning and using the pre-trained network as a fixed feature extractor, among others. In this paper we propose a straightforward alternative: Side-Tuning.Side-tuning adapts a pretrained network by training a lightweight \"side\" network that is fused with the (unchanged) pre-rained network using a simple additive process.This simple method works as well as or better than existing solutions while it resolves some of the basic issues with fine-tuning, fixed features, and several other common baselines.In particular, side-tuning is less prone to overfitting when little training data is available, yields better results than using a fixed feature extractor, and doesn't suffer from catastrophic forgetting in lifelong learning. We demonstrate the performance of side-tuning under a diverse set of scenarios, including lifelong learning (iCIFAR, Taskonomy), reinforcement learning, imitation learning (visual navigation in Habitat), NLP question-answering (SQuAD v2), and single-task transfer learning (Taskonomy), with consistently promising results.", "target": ["Il side-tuning adatta una rete pre-addestrata addestrando una rete leggera \"laterale\" che viene fusa con la rete pre-addestrata (mantenuta invariata) utilizzando un semplice processo additivo."]} +{"source": "In this paper, we present a technique for generating artificial datasets that retain statistical properties of the real data while providing differential privacy guarantees with respect to this data.We include a Gaussian noise layer in the discriminator of a generative adversarial network to make the output and the gradients differentially private with respect to the training data, and then use the generator component to synthesise privacy-preserving artificial dataset.Our experiments show that under a reasonably small privacy budget we are able to generate data of high quality and successfully train machine learning models on this artificial data.", "target": ["Addestrare le GAN con privacy differenziale per generare dataset artificiali che preservino la privacy."]} +{"source": "This paper presents two methods to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network.Unlike convolutional studies that visualize image appearances corresponding to the network output or a neural activation from a global perspective, our research aims to clarify how a certain input unit (dimension) collaborates with other units (dimensions) to constitute inference patterns of the neural network and thus contribute to the network output.The analysis of local contextual effects w.r.t. certain input units is of special values in real applications.In particular, we used our methods to explain the gaming strategy of the alphaGo Zero model in experiments, and our method successfully disentangled the rationale of each move during the game.", "target": ["Questo articolo presenta metodi per distinguere e interpretare gli effetti contestuali che sono codificati in una deep neural network."]} +{"source": "The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup.", "target": ["Si propone un nuovo metodo basato sull'attention guidata per far rispettare la sparsità nelle deep neural network."]} +{"source": "Giving provable guarantees for learning neural networks is a core challenge of machine learning theory.Most prior work gives parameter recovery guarantees for one hidden layer networks, however, the networks used in practice have multiple non-linear layers.In this work, we show how we can strengthen such results to deeper networks -- we address the problem of uncovering the lowest layer in a deep neural network under the assumption that the lowest layer uses a high threshold before applying the activation, the upper network can be modeled as a well-behaved polynomial and the input distribution is gaussian.", "target": ["Proviamo a recuperare il layer più basso in una deep neural network assumendo che il layer più basso utilizzi un'attivazione ad \"alta soglia\" e che la rete di cui sopra sia un polinomio \"well-behaved\"."]} +{"source": "Federated learning involves training and effectively combining machine learning models from distributed partitions of data (i.e., tasks) on edge devices, and be naturally viewed as a multi- task learning problem.While Federated Averaging (FedAvg) is the leading optimization method for training non-convex models in this setting, its behavior is not well understood in realistic federated settings when the devices/tasks are statistically heterogeneous, i.e., where each device collects data in a non-identical fashion.In this work, we introduce a framework, called FedProx, to tackle statistical heterogeneity.FedProx encompasses FedAvg as a special case.We provide convergence guarantees for FedProx through a device dissimilarity assumption.Our empirical evaluation validates our theoretical analysis and demonstrates the improved robustness and stability of FedProx for learning in heterogeneous networks.", "target": ["Introduciamo FedProx, un framework per affrontare l'eterogeneità statistica in setting federati con garanzie di convergenza e migliore robustezza e stabilità."]} +{"source": "Referential games offer a grounded learning environment for neural agents which accounts for the fact that language is functionally used to communicate.However, they do not take into account a second constraint considered to be fundamental for the shape of human language: that it must be learnable by new language learners and thus has to overcome a transmission bottleneck.In this work, we insert such a bottleneck in a referential game, by introducing a changing population of agents in which new agents learn by playing with more experienced agents.We show that mere cultural transmission results in a substantial improvement in language efficiency and communicative success, measured in convergence speed, degree of structure in the emerged languages and within-population consistency of the language.However, as our core contribution, we show that the optimal situation is to co-evolve language and agents.When we allow the agent population to evolve through genotypical evolution, we achieve across the board improvements on all considered metrics.These results stress that for language emergence studies cultural evolution is important, but also the suitability of the architecture itself should be considered.", "target": ["Permettiamo sia l'evoluzione culturale del linguaggio che l'evoluzione genetica degli agenti in un gioco referenziale, utilizzando un nuovo motore di trasmissione del linguaggio."]} +{"source": "The need for large amounts of training image data with clearly defined features is a major obstacle to applying generative adversarial networks(GAN) on image generation where training data is limited but diverse, since insufficient latent feature representation in the already scarce data often leads to instability and mode collapse during GAN training.To overcome the hurdle of limited data when applying GAN to limited datasets, we propose in this paper the strategy of \\textit{parallel recurrent data augmentation}, where the GAN model progressively enriches its training set with sample images constructed from GANs trained in parallel at consecutive training epochs.Experiments on a variety of small yet diverse datasets demonstrate that our method, with little model-specific considerations, produces images of better quality as compared to the images generated without such strategy.The source code and generated images of this paper will be made public after review.", "target": ["Abbiamo introdotto un nuovo metodo di data augmentation semplice ed efficiente che aumenta le prestazioni delle GAN esistenti quando i dati di training sono limitati e diversi."]} +{"source": "We develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently regularizing to allow imputation of latent membrane potentials from partial calcium fluorescence imaging observations.This is the first attempt we know of to ``complete the circle,'' where an anatomically grounded whole-connectome simulator is used to impute a time-varying ``brain'' state at single-cell fidelity from covariates that are measurable in practice. Using state of the art Bayesian machine learning methods to condition on readily obtainable data, our method paves the way for neuroscientists to recover interpretable connectome-wide state representations, automatically estimate physiologically relevant parameter values from data, and perform simulations investigating intelligent lifeforms in silico.", "target": ["Sviluppiamo un simulatore del corpo e dell'intero connettoma per C. elegans e mostriamo lo spazio di stato congiunto e l'inferenza dei parametri nel simulatore."]} +{"source": "The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance.However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph embeddings.Inspired by the Capsule Neural Network (CapsNet), we propose the Capsule Graph Neural Network (CapsGNN), which adopts the concept of capsules to address the weakness in existing GNN-based graph embeddings algorithms.By extracting node features in the form of capsules, routing mechanism can be utilized to capture important information at the graph level.As a result, our model generates multiple embeddings for each graph to capture graph properties from different aspects.The attention module incorporated in CapsGNN is used to tackle graphs with various sizes which also enables the model to focus on critical parts of the graphs.Our extensive evaluations with 10 graph-structured datasets demonstrate that CapsGNN has a powerful mechanism that operates to capture macroscopic properties of the whole graph by data-driven.It outperforms other SOTA techniques on several graph classification tasks, by virtue of the new instrument.", "target": ["Ispirati da CapsNet, proponiamo una nuova architettura per l'embedding dei grafi sulla base delle feature dei nodi estratte da GNN."]} +{"source": "We introduce a novel framework for generative models based on Restricted Kernel Machines (RKMs) with multi-view generation and uncorrelated feature learning capabilities, called Gen-RKM.To incorporate multi-view generation, this mechanism uses a shared representation of data from various views.The mechanism is flexible to incorporate both kernel-based, (deep) neural network and convolutional based models within the same setting.To update the parameters of the network, we propose a novel training procedure which jointly learns the features and shared representation.Experiments demonstrate the potential of the framework through qualitative evaluation of generated samples.", "target": ["Gen-RKM: un nuovo framework per i modelli generativi usando Restricted Kernel Machines con generazione multi-vista e apprendimento di feature non correlate."]} +{"source": "Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018).This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives.The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models.However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work.", "target": ["Confrontiamo molti task e combinazioni di task per il pretraining di BiLSTM a livello di frase per task di NLP. Language modeling è il singolo miglior task di pretraining, ma anche le baseline semplici si comportano bene."]} +{"source": "In this paper, we study the adversarial attack and defence problem in deep learning from the perspective of Fourier analysis.We first explicitly compute the Fourier transform of deep ReLU neural networks and show that there exist decaying but non-zero high frequency components in the Fourier spectrum of neural networks.We then demonstrate that the vulnerability of neural networks towards adversarial samples can be attributed to these insignificant but non-zero high frequency components.Based on this analysis, we propose to use a simple post-averaging technique to smooth out these high frequency components to improve the robustness of neural networks against adversarial attacks.Experimental results on the ImageNet and the CIFAR-10 datasets have shown that our proposed method is universally effective to defend many existing adversarial attacking methods proposed in the literature, including FGSM, PGD, DeepFool and C&W attacks.Our post-averaging method is simple since it does not require any re-training, and meanwhile it can successfully defend over 80-96% of the adversarial samples generated by these methods without introducing significant performance degradation (less than 2%) on the original clean images.", "target": ["Un'intuizione sulla ragione dell'adversarial vulnerability ed un metodo di difesa efficace contro gli adversarial attack."]} +{"source": "Reinforcement learning methods that continuously learn neural networks by episode generation with game tree search have been successful in two-person complete information deterministic games such as chess, shogi, and Go.However, there are only reports of practical cases and there are little evidence to guarantee the stability and the final performance of learning process.In this research, the coordination of episode generation was focused on.By means of regarding the entire system as game tree search, the new method can handle the trade-off between exploitation and exploration during episode generation.The experiments with a small problem showed that it had robust performance compared to the existing method, Alpha Zero.", "target": ["Applicare Monte Carlo Tree Search alla generazione di episodi in Alpha Zero"]} +{"source": "Message-passing neural networks (MPNNs) have been successfully applied in a wide variety of applications in the real world.However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs.Few studies have noticed the weaknesses from different perspectives.From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph.The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation.We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs.Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs.", "target": ["Per le graph neural network, l'aggregazione su un grafo può beneficiare di uno spazio continuo sottostante il grafo."]} +{"source": "We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards.We introduce a novel iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far.Then, we synthesize new programs and add them to the priority queue by sampling from the RNN.We benchmark our algorithm called priority queue training (PQT) against genetic algorithm and reinforcement learning baselines on a simple but expressive Turing complete programming language called BF.Our experimental results show that our deceptively simple PQT algorithm significantly outperforms the baselines.By adding a program length penalty to the reward function, we are able to synthesize short, human readable programs.", "target": ["Usiamo un semplice algoritmo di ricerca che coinvolge una RNN e una coda di priorità per trovare soluzioni ai task di coding."]} +{"source": "We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform.Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost.Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution.The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed.", "target": ["Presentiamo la graph wavelet neural network (GWNN), una nuova graph convolutional neural network (CNN), che sfrutta la trasformazione wavelet a grafo per affrontare la lacuna dei precedenti metodi CNN a grafo spettrale che dipendono dalla trasformata di Fourier del grafo."]} +{"source": "Learning rate decay (lrDecay) is a \\emph{de facto} technique for training modern neural networks.It starts with a large learning rate and then decays it multiple times.It is empirically observed to help both optimization and generalization.Common beliefs in how lrDecay works come from the optimization analysis of (Stochastic) Gradient Descent:1) an initially large learning rate accelerates training or helps the network escape spurious local minima;2) decaying the learning rate helps the network converge to a local minimum and avoid oscillation.Despite the popularity of these common beliefs, experiments suggest that they are insufficient in explaining the general effectiveness of lrDecay in training modern neural networks that are deep, wide, and nonconvex.We provide another novel explanation: an initially large learning rate suppresses the network from memorizing noisy data while decaying the learning rate improves the learning of complex patterns.The proposed explanation is validated on a carefully-constructed dataset with tractable pattern complexity.And its implication, that additional patterns learned in later stages of lrDecay are more complex and thus less transferable, is justified in real-world datasets.We believe that this alternative explanation will shed light into the design of better training strategies for modern neural networks.", "target": ["Forniamo un'ulteriore nuova spiegazione del decadimento del learning rate: un learning rate inizialmente grande evita la memorizzazione di dati rumorosi da parte della rete, mentre il decadimento del learning rate migliora l'apprendimento di modelli complessi."]} +{"source": "We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning.We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting.We propose an exploration strategy based on upper-confidence bounds (UCB).Our experiments show significant gains on the Atari benchmark.", "target": ["L'adattamento dell'esplorazione UCB all'ensemble Q-learning apporta dei miglioramenti rispetto ai metodi precedenti come Double DQN, A3C+ sul benchmark Atari"]} +{"source": "We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids.To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck.We show that it is possible to train this model entirely offline to compress thousands of expert policies and learn a motor primitive embedding space.The trained neural probabilistic motor primitive system can perform one-shot imitation of whole-body humanoid behaviors, robustly mimicking unseen trajectories.Additionally, we demonstrate that it is also straightforward to train controllers to reuse the learned motor primitive space to solve tasks, and the resulting movements are relatively naturalistic.To support the training of our model, we compare two approaches for offline policy cloning, including an experience efficient method which we call linear feedback policy cloning.We encourage readers to view a supplementary video (https://youtu.be/CaDEf-QcKwA ) summarizing our results.", "target": ["Le primitive motorie probabilistiche neurali comprimono le policy di tracking del movimento in un modello flessibile capace di imitazione e riutilizzo one-shot come controller di basso livello."]} +{"source": "Data augmentation is a useful technique to enlarge the size of the training set and prevent overfitting for different machine learning tasks when training data is scarce.However, current data augmentation techniques rely heavily on human design and domain knowledge, and existing automated approaches are yet to fully exploit the latent features in the training dataset.In this paper we propose \\textit{Parallel Adaptive GAN Data Augmentation}(PAGANDA), where the training set adaptively enriches itself with sample images automatically constructed from Generative Adversarial Networks (GANs) trained in parallel.We demonstrate by experiments that our data augmentation strategy, with little model-specific considerations, can be easily adapted to cross-domain deep learning/machine learning tasks such as image classification and image inpainting, while significantly improving model performance in both tasks.Our source code and experimental details are available at \\url{https://github.com/miaojiang1987/k-folder-data-augmentation-gan/}.", "target": ["Presentiamo una data augmentation automatica e adattiva che funziona per più task diversi."]} +{"source": "Deep neural networks with millions of parameters may suffer from poor generalizations due to overfitting.To mitigate the issue, we propose a new regularization method that penalizes the predictive distribution between similar samples.In particular, we distill the predictive distribution between different samples of the same label and augmented samples of the same source during training.In other words, we regularize the dark knowledge (i.e., the knowledge on wrong predictions) of a single network, i.e., a self-knowledge distillation technique, to force it output more meaningful predictions. We demonstrate the effectiveness of the proposed method via experiments on various image classification tasks: it improves not only the generalization ability, but also the calibration accuracy of modern neural networks.", "target": ["Proponiamo una nuova tecnica di regolarizzazione basata sulla knowledge distillation."]} +{"source": "In this paper, we consider the specific problem of word-level language modeling and investigate strategies for regularizing and optimizing LSTM-based models.We propose the weight-dropped LSTM, which uses DropConnect on hidden-to-hidden weights, as a form of recurrent regularization.Further, we introduce NT-ASGD, a non-monotonically triggered (NT) variant of the averaged stochastic gradient method (ASGD), wherein the averaging trigger is determined using a NT condition as opposed to being tuned by the user.Using these and other regularization strategies, our ASGD Weight-Dropped LSTM (AWD-LSTM) achieves state-of-the-art word level perplexities on two data sets: 57.3 on Penn Treebank and 65.8 on WikiText-2.In exploring the effectiveness of a neural cache in conjunction with our proposed model, we achieve an even lower state-of-the-art perplexity of 52.8 on Penn Treebank and 52.0 on WikiText-2.We also explore the viability of the proposed regularization and optimization strategies in the context of the quasi-recurrent neural network (QRNN) and demonstrate comparable performance to the AWD-LSTM counterpart.The code for reproducing the results is open sourced and is available at https://github.com/salesforce/awd-lstm-lm.", "target": ["Strategie efficaci di regolarizzazione e ottimizzazione per language model basati su LSTM raggiungono SOTA su PTB e WT2."]} +{"source": "Various methods of measuring unit selectivity have been developed with the aim of better understanding how neural networks work. But the different measures provide divergent estimates of selectivity, and this has led to different conclusions regarding the conditions in which selective object representations are learned and the functional relevance of these representations.In an attempt to better characterize object selectivity, we undertake a comparison of various selectivity measures on a large set of units in AlexNet, including localist selectivity, precision, class-conditional mean activity selectivity (CCMAS), network dissection, the human interpretation of activation maximization (AM) images, and standard signal-detection measures. We find that the different measures provide different estimates of object selectivity, with precision and CCMAS measures providing misleadingly high estimates.Indeed, the most selective units had a poor hit-rate or a high false-alarm rate (or both) in object classification, making them poor object detectors. We fail to find any units that are even remotely as selective as the 'grandmother cell' units reported in recurrent neural networks.In order to generalize these results, we compared selectivity measures on a few units in VGG-16 and GoogLeNet trained on the ImageNet or Places-365 datasets that have been described as 'object detectors'.Again, we find poor hit-rates and high false-alarm rates for object classification.", "target": ["Alla ricerca di object detector utilizzando diverse misure di selettività; le CNN sono leggermente selettive, ma non abbastanza per essere definite object detector."]} +{"source": "The folding structure of the DNA molecule combined with helper molecules, also referred to as the chromatin, is highly relevant for the functional properties of DNA.The chromatin structure is largely determined by the underlying primary DNA sequence, though the interaction is not yet fully understood.In this paper we develop a convolutional neural network that takes an image-representation of primary DNA sequence as its input, and predicts key determinants of chromatin structure.The method is developed such that it is capable of detecting interactions between distal elements in the DNA sequence, which are known to be highly relevant.Our experiments show that the method outperforms several existing methods both in terms of prediction accuracy and training time.", "target": ["Un metodo per trasformare sequenze di DNA in immagini 2D utilizzando le curve di Hilbert di riempimento dello spazio per migliorare la forza delle CNN"]} +{"source": "This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster.The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning.We begin by reducing categorical information to pairwise constraints, which only considers whether two instances belong to the same class or not (pairwise semantic similarity).This similarity is category-agnostic and can be learned from data in the source domain using a similarity network.We then present two novel approaches for performing transfer learning using this similarity function.First, for unsupervised domain adaptation, we design a new loss function to regularize classification with a constrained clustering loss, hence learning a clustering network with the transferred similarity metric generating the training inputs.Second, for cross-task learning (i.e., unsupervised clustering with unseen categories), we propose a framework to reconstruct and estimate the number of semantic clusters, again using the clustering network.Since the similarity network is noisy, the key is to use a robust clustering algorithm, and we show that our formulation is more robust than the alternative constrained and unconstrained clustering approaches.Using this method, we first show state of the art results for the challenging cross-task problem, applied on Omniglot and ImageNet.Our results show that we can reconstruct semantic clusters with high accuracy.We then evaluate the performance of cross-domain transfer using images from the Office-31 and SVHN-MNIST tasks and present top accuracy on both datasets. Our approach doesn't explicitly deal with domain discrepancy.If we combine with a domain adaptation loss, it shows further improvement.", "target": ["Un obiettivo di clustering apprendibile per facilitare il transfer learning tra domini e task"]} +{"source": "Recent advances in cross-lingual word embeddings have primarily relied on mapping-based methods, which project pretrained word embeddings from different languages into a shared space through a linear transformation.However, these approaches assume word embedding spaces are isomorphic between different languages, which has been shown not to hold in practice (Søgaard et al., 2018), and fundamentally limits their performance.This motivates investigating joint learning methods which can overcome this impediment, by simultaneously learning embeddings across languages via a cross-lingual term in the training objective.Given the abundance of parallel data available (Tiedemann, 2012), we propose a bilingual extension of the CBOW method which leverages sentence-aligned corpora to obtain robust cross-lingual word and sentence representations.Our approach significantly improves cross-lingual sentence retrieval performance over all other approaches, as well as convincingly outscores mapping methods while maintaining parity with jointly trained methods on word-translation.It also achieves parity with a deep RNN method on a zero-shot cross-lingual document classification task, requiring far fewer computational resources for training and inference.As an additional advantage, our bilingual method also improves the quality of monolingual word vectors despite training on much smaller datasets. We make our code and models publicly available.", "target": ["Metodo joint per l'apprendimento di embedding multilingue con prestazioni allo stato dell'arte per cross-ingual task e qualità monolingua"]} +{"source": "To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics:(a) it should build an abstract state representing the condition of the world;(b) it should form a belief which represents uncertainty on the world;(c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction.Motivated by the absence of a model satisfying all these requirements, we propose TD-VAE, a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions.TD-VAE is trained on pairs of temporally separated time points, using an analogue of temporal difference learning used in reinforcement learning.", "target": ["Modello generativo di dati temporali, che costruisce online il belief state, opera nello spazio latente, fa previsioni saltuarie e rollout di stati."]} +{"source": "This paper introduces an information theoretic co-training objective for unsupervised learning. We consider the problem of predicting the future. Rather than predict future sensations (image pixels or sound waves) we predict ``hypotheses'' to be confirmed by future sensations. More formally, we assume a population distribution on pairs $(x,y)$ where we can think of $x$ as a past sensation and $y$ as a future sensation. We train both a predictor model $P_\\Phi(z|x)$ and a confirmation model $P_\\Psi(z|y)$ where we view $z$ as hypotheses (when predicted) or facts (when confirmed). For a population distribution on pairs $(x,y)$ we focus on the problem of measuring the mutual information between $x$ and $y$. By the data processing inequality this mutual information is at least as large as the mutual information between $x$ and $z$ under the distribution on triples $(x,z,y)$ defined by the confirmation model $P_\\Psi(z|y)$.The information theoretic training objective for $P_\\Phi(z|x)$ and $P_\\Psi(z|y)$ can be viewed as a form of co-training where we want the prediction from $x$ to match the confirmation from $y$.We give experiments on applications to learning phonetics on the TIMIT dataset.", "target": ["Presenta un obiettivo di training basato sulla teoria dell'informazione per il co-training e dimostra la sua potenza nell'apprendimento unsupervised della fonetica."]} +{"source": "Generative models for singing voice have been mostly concerned with the task of \"singing voice synthesis,\" i.e., to produce singing voice waveforms given musical scores and text lyrics.In this work, we explore a novel yet challenging alternative: singing voice generation without pre-assigned scores and lyrics, in both training and inference time.In particular, we experiment with three different schemes:1) free singer, where the model generates singing voices without taking any conditions;2) accompanied singer, where the model generates singing voices over a waveform of instrumental music; and3) solo singer, where the model improvises a chord sequence first and then uses that to generate voices.We outline the associated challenges and propose a pipeline to tackle these new tasks.This involves the development of source separation and transcription models for data preparation, adversarial networks for audio generation, and customized metrics for evaluation.", "target": ["I nostri modelli generano voci cantanti senza testi e spartiti. Prendono l'accompagnamento come input e producono voci cantate."]} +{"source": "The carbon footprint of natural language processing (NLP) research has been increasing in recent years due to its reliance on large and inefficient neural network implementations.Distillation is a network compression technique which attempts to impart knowledge from a large model to a smaller one.We use teacher-student distillation to improve the efficiency of the Biaffine dependency parser which obtains state-of-the-art performance with respect to accuracy and parsing speed (Dozat & Manning, 2016). When distilling to 20% of the original model’s trainable parameters, we only observe an average decrease of ∼1 point for both UAS and LAS across a number of diverse Universal Dependency treebanks while being 2.26x (1.21x) faster than the baseline model on CPU (GPU) at inference time.We also observe a small increase in performance when compressing to 80% for some treebanks. Finally, through distillation we attain a parser which is not only faster but also more accurate than the fastest modern parser on the Penn Treebank.", "target": ["Aumentiamo l'efficienza dei dependency parser basati su rete neurale con la teacher-student distillation."]} +{"source": "While autoencoders are a key technique in representation learning for continuous structures, such as images or wave forms, developing general-purpose autoencoders for discrete structures, such as text sequence or discretized images, has proven to be more challenging.In particular, discrete inputs make it more difficult to learn a smooth encoder that preserves the complex local relationships in the input space.In this work, we propose an adversarially regularized autoencoder (ARAE) with the goal of learning more robust discrete-space representations.ARAE jointly trains both a rich discrete-space encoder, such as an RNN, and a simpler continuous space generator function, while using generative adversarial network (GAN) training to constrain the distributions to be similar.This method yields a smoother contracted code space that maps similar inputs to nearby codes, and also an implicit latent variable GAN model for generation.Experiments on text and discretized images demonstrate that the GAN model produces clean interpolations and captures the multimodality of the original space, and that the autoencoder produces improvements in semi-supervised learning as well as state-of-the-art results in unaligned text style transfer task using only a shared continuous-space representation.", "target": ["Gli Adversarially Regularized Autoencoders apprendono rappresentazioni smooth di strutture discrete permettendo risultati interessanti nella generazione di testo, come il transfer di stile non allineato, l'apprendimento semi-supervised e l'interpolazione e l'aritmetica nello spazio latente."]} +{"source": "When data arise from multiple latent subpopulations, machine learning frameworks typically estimate parameter values independently for each sub-population.In this paper, we proposeto overcome these limits by considering samples as tasks in a multitask learning framework.", "target": ["Presentiamo un metodo per stimare collezioni di modelli di regressione in cui ogni modello è personalizzato per un singolo sample."]} +{"source": "In this work, we first conduct mathematical analysis on the memory, which isdefined as a function that maps an element in a sequence to the current output,of three RNN cells; namely, the simple recurrent neural network (SRN), the longshort-term memory (LSTM) and the gated recurrent unit (GRU).Based on theanalysis, we propose a new design, called the extended-long short-term memory(ELSTM), to extend the memory length of a cell.Next, we present a multi-taskRNN model that is robust to previous erroneous predictions, called the dependentbidirectional recurrent neural network (DBRNN), for the sequence-in-sequenceout(SISO) problem.Finally, the performance of the DBRNN model with theELSTM cell is demonstrated by experimental results.", "target": ["Una cella di rete neurale ricorrente con memoria a breve termine estesa e un modello RNN multi-task per problemi di sequence-in-sequence-out"]} +{"source": "Many recently trained neural networks employ large numbers of parameters to achieve good performance.One may intuitively use the number of parameters required as a rough gauge of the difficulty of a problem.But how accurate are such notions?How many parameters are really needed?In this paper we attempt to answer this question by training networks not in their native parameter space, but instead in a smaller, randomly oriented subspace.We slowly increase the dimension of this subspace, note at which dimension solutions first appear, and define this to be the intrinsic dimension of the objective landscape.The approach is simple to implement, computationally tractable, and produces several suggestive conclusions.Many problems have smaller intrinsic dimensions than one might suspect, and the intrinsic dimension for a given dataset varies little across a family of models with vastly different sizes.This latter result has the profound implication that once a parameter space is large enough to solve a problem, extra parameters serve directly to increase the dimensionality of the solution manifold.Intrinsic dimension allows some quantitative comparison of problem difficulty across supervised, reinforcement, and other types of learning where we conclude, for example, that solving the inverted pendulum problem is 100 times easier than classifying digits from MNIST, and playing Atari Pong from pixels is about as hard as classifying CIFAR-10.In addition to providing new cartography of the objective landscapes wandered by parameterized models, the method is a simple technique for constructively obtaining an upper bound on the minimum description length of a solution.A byproduct of this construction is a simple approach for compressing networks, in some cases by more than 100 times.", "target": ["Performiamo il training in sottospazi casuali dello spazio dei parametri per misurare quante dimensioni sono realmente necessarie per trovare una soluzione."]} +{"source": "Graph Neural Networks as a combination of Graph Signal Processing and Deep Convolutional Networks shows great power in pattern recognition in non-Euclidean domains.In this paper, we propose a new method to deploy two pipelines based on the duality of a graph to improve accuracy.By exploring the primal graph and its dual graph where nodes and edges can be treated as one another, we have exploited the benefits of both vertex features and edge features.As a result, we have arrived at a framework that has great potential in both semisupervised and unsupervised learning.", "target": ["Un modello primale e duale su graph neural network per l'apprendimento semi-supervised"]} +{"source": "We describe two end-to-end autoencoding models for semi-supervised graph-based dependency parsing.The first model is a Local Autoencoding Parser (LAP) encoding the input using continuous latent variables in a sequential manner; The second model is a Global Autoencoding Parser (GAP) encoding the input into dependency trees as latent variables, with exact inference.Both models consist of two parts: an encoder enhanced by deep neural networks (DNN) that can utilize the contextual information to encode the input into latent variables, and a decoder which is a generative model able to reconstruct the input.Both LAP and GAP admit a unified structure with different loss functions for labeled and unlabeled data with shared parameters.We conducted experiments on WSJ and UD dependency parsing data sets, showing that our models can exploit the unlabeled data to boost the performance given a limited amount of labeled data.", "target": ["Descriviamo due parser autoencoding end-to-end per dependency parsing semi-supervisionato basato su grafi."]} +{"source": "We improve previous end-to-end differentiable neural networks (NNs) with fastweight memories.A gate mechanism updates fast weights at every time step ofa sequence through two separate outer-product-based matrices generated by slowparts of the net.The system is trained on a complex sequence to sequence variationof the Associative Retrieval Problem with roughly 70 times more temporalmemory (i.e. time-varying variables) than similar-sized standard recurrent NNs(RNNs).In terms of accuracy and number of parameters, our architecture outperformsa variety of RNNs, including Long Short-Term Memory, Hypernetworks,and related fast weight architectures.", "target": ["Una rete Fast Weight migliorata che mostra risultati migliori su un toy task generale."]} +{"source": "The field of deep learning has been craving for an optimization method that shows outstanding property for both optimization and generalization. We propose a method for mathematical optimization based on flows along geodesics, that is, the shortest paths between two points, with respect to the Riemannian metric induced by a non-linear function.In our method, the flows refer to Exponentially Decaying Flows (EDF), as they can be designed to converge on the local solutions exponentially.In this paper, we conduct experiments to show its high performance on optimization benchmarks (i.e., convergence properties), as well as its potential for producing good machine learning benchmarks (i.e., generalization properties).", "target": ["Introduzione di un nuovo metodo di ottimizzazione e la sua applicazione al deep learning."]} +{"source": "We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network.Along with this general class of ansatze, we introduce further specialized architectures, namely, Quantum Graph Recurrent Neural Networks (QGRNN) and Quantum Graph Convolutional Neural Networks (QGCNN).We provide four example applications of QGNN's: learning Hamiltonian dynamics of quantum systems, learning how to create multipartite entanglement in a quantum network, unsupervised learning for spectral clustering, and supervised learning for graph isomorphism classification.", "target": ["Introduzione di una nuova classe di reti neurali quantistiche per l'apprendimento di rappresentazioni basate su grafi su computer quantistici."]} +{"source": "Data breaches involve information being accessed by unauthorized parties.Our research concerns user perception of data breaches, especially issues relating to accountability.A preliminary study indicated many people had weak understanding of the issues, and felt they themselves were somehow responsible.We speculated that this impression might stem from organizational communication strategies.We therefore compared texts from organizations with external sources, such as the news media.This suggested that organizations use well-known crisis communication methods to reduce their reputational damage, and that these strategies align with repositioning of the narrative elements involved in the story.We then conducted a quantitative study, asking participants to rate either organizational texts or news texts about breaches.The findings of this study were in line with our document analysis, and suggest that organizational communication affects the users' perception of victimization, attitudes in data protection, and accountability.Our study suggests some software design and legal implications supporting users to protect themselves and develop better mental models of security breaches.", "target": ["In questo articolo, abbiamo testato l'influenza delle strategie di comunicazione sui modelli mentali degli utenti relativi ad una violazione dei dati."]} +{"source": "Goal recognition is the problem of inferring the correct goal towards which an agent executes a plan, given a set of goal hypotheses, a domain model, and a (possibly noisy) sample of the plan being executed. This is a key problem in both cooperative and competitive agent interactions and recent approaches have produced fast and accurate goal recognition algorithms. In this paper, we leverage advances in operator-counting heuristics computed using linear programs over constraints derived from classical planning problems to solve goal recognition problems. Our approach uses additional operator-counting constraints derived from the observations to efficiently infer the correct goal, and serves as basis for a number of further methods with additional constraints.", "target": ["Un approccio di riconoscimento degli obiettivi basato sull'euristica del conteggio degli operatori usato per tenere conto del rumore nel dataset."]} +{"source": "It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts.To help address this, we propose using knowledge distillation where single-task models teach a multi-task model.We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers.We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark.Our method consistently improves over standard single-task and multi-task training.", "target": ["Distillare i modelli a task singolo in un modello multitasking migliora le prestazioni di natural language understanding."]} +{"source": "The detection of out of distribution samples for image classification has been widely researched.Safety critical applications, such as autonomous driving, would benefit from the ability to localise the unusual objects causing the image to be out of distribution.This paper adapts state-of-the-art methods for detecting out of distribution images for image classification to the new task of detecting out of distribution pixels, which can localise the unusual objects.It further experimentally compares the adapted methods on two new datasets derived from existing semantic segmentation datasets using PSPNet and DeeplabV3+ architectures, as well as proposing a new metric for the task.The evaluation shows that the performance ranking of the compared methods does not transfer to the new task and every method performs significantly worse than their image-level counterparts.", "target": ["Valutazione dei metodi di rilevamento di out-of-distribution a livello di pixel su due nuovi dataset del mondo reale utilizzando PSPNet e DeeplabV3+."]} +{"source": "We present a domain adaptation method for transferring neural representations from label-rich source domains to unlabeled target domains.Recent adversarial methods proposed for this task learn to align features across domains by ``fooling'' a special domain classifier network.However, a drawback of this approach is that the domain classifier simply labels the generated features as in-domain or not, without considering the boundaries between classes.This means that ambiguous target features can be generated near class boundaries, reducing target classification accuracy.We propose a novel approach, Adversarial Dropout Regularization (ADR), which encourages the generator to output more discriminative features for the target domain.Our key idea is to replace the traditional domain critic with a critic that detects non-discriminative features by using dropout on the classifier network.The generator then learns to avoid these areas of the feature space and thus creates better features.We apply our ADR approach to the problem of unsupervised domain adaptation for image classification and semantic segmentation tasks, and demonstrate significant improvements over the state of the art.", "target": ["Presentiamo un nuovo metodo adversarial per adattare le rappresentazioni neurali basate su un critico che rileva le feature non discriminatorie."]} +{"source": "The use of deep learning for a wide range of data problems has increased the need for understanding and diagnosing these models, and deep learning interpretation techniques have become an essential tool for data analysts.Although numerous model interpretation methods have been proposed in recent years, most of these procedures are based on heuristics with little or no theoretical guarantees.In this work, we propose a statistical framework for saliency estimation for black box computer vision models.We build a model-agnostic estimation procedure that is statistically consistent and passes the saliency checks of Adebayo et al. (2018).Our method requires solving a linear program, whose solution can be efficiently computed in polynomial time.Through our theoretical analysis, we establish an upper bound on the number of model evaluations needed to recover the region of importance with high probability, and build a new perturbation scheme for estimation of local gradients that is shown to be more efficient than the commonly used random perturbation schemes.Validity of the new method is demonstrated through sensitivity analysis.", "target": ["Proponiamo un framework statistico e una procedura teoricamente coerente per la stima della salienza."]} +{"source": "We explore the idea of compositional set embeddings that can be used to infer notjust a single class, but the set of classes associated with the input data (e.g., image,video, audio signal).This can be useful, for example, in multi-object detection inimages, or multi-speaker diarization (one-shot learning) in audio.In particular, wedevise and implement two novel models consisting of (1) an embedding functionf trained jointly with a “composite” function g that computes set union opera-tions between the classes encoded in two embedding vectors; and (2) embeddingf trained jointly with a “query” function h that computes whether the classes en-coded in one embedding subsume the classes encoded in another embedding.Incontrast to prior work, these models must both perceive the classes associatedwith the input examples, and also encode the relationships between different classlabel sets.In experiments conducted on simulated data, OmniGlot, and COCOdatasets, the proposed composite embedding models outperform baselines basedon traditional embedding approaches.", "target": ["Abbiamo esplorato come un nuovo metodo di compositional set embedding può sia percepire che rappresentare non solo una singola classe ma un intero insieme di classi che è associato ai dati di input."]} +{"source": "In this work, we propose a goal-driven collaborative task that contains language, vision, and action in a virtual environment as its core components.Specifically, we develop a Collaborative image-Drawing game between two agents, called CoDraw.Our game is grounded in a virtual world that contains movable clip art objects.The game involves two players: a Teller and a Drawer.The Teller sees an abstract scene containing multiple clip art pieces in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip art pieces.The two players communicate via two-way communication using natural language.We collect the CoDraw dataset of ~10K dialogs consisting of ~138K messages exchanged between human agents.We define protocols and metrics to evaluate the effectiveness of learned agents on this testbed, highlighting the need for a novel \"crosstalk\" condition which pairs agents trained independently on disjoint subsets of the training data for evaluation.We present models for our task, including simple but effective baselines and neural network approaches trained using a combination of imitation learning and goal-driven training.All models are benchmarked using both fully automated evaluation and by playing the game with live human agents.", "target": ["Introduciamo un dataset, modelli e protocolli di training + valutazione per un task di disegno collaborativo che permette di studiare la generazione e la comprensione del linguaggio guidato dall'obiettivo e basato su percezione + azione."]} +{"source": "Presence of bias and confounding effects is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in the recent years.Such challenges range from spurious associations of confounding variables in medical studies to the bias of race in gender or face recognition systems.One solution is to enhance datasets and organize them such that they do not reflect biases, which is a cumbersome and intensive task.The alternative is to make use of available data and build models considering these biases.Traditional statistical methods apply straightforward techniques such as residualization or stratification to precomputed features to account for confounding variables.However, these techniques are not in general applicable to end-to-end deep learning methods.In this paper, we propose a method based on the adversarial training strategy to learn discriminative features unbiased and invariant to the confounder(s).This is enabled by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and learned features.We apply our method to a synthetic, a medical diagnosis, and a gender classification (Gender Shades) dataset.Our results show that the learned features by our method not only result in superior prediction performance but also are uncorrelated with the bias or confounder variables.The code is available at http://blinded_for_review/.", "target": ["Proponiamo un metodo basato sulla strategia di adversarial training per imparare feature discriminative imparziali e invarianti ai confounder incorporando una loss che incoraggia una correlazione svanita tra il bias e le feature apprese."]} +{"source": "Existing neural question answering (QA) models are required to reason over and draw complicated inferences from a long context for most large-scale QA datasets.However, if we view QA as a combined retrieval and reasoning task, we can assume the existence of a minimal context which is necessary and sufficient to answer a given question.Recent work has shown that a sentence selector module that selects a shorter context and feeds it to the downstream QA model achieves performance comparable to a QA model trained on full context, while also being more interpretable.Recent work has also shown that most state-of-the-art QA models break when adversarially generated sentences are appended to the context.While humans are immune to such distractor sentences, QA models get easily misled into selecting answers from these sentences.We hypothesize that the sentence selector module can filter out extraneous context, thereby allowing the downstream QA model to focus and reason over the parts of the context that are relevant to the question.In this paper, we show that the sentence selector itself is susceptible to adversarial inputs.However, we demonstrate that a pipeline consisting of a sentence selector module followed by the QA model can be made more robust to adversarial attacks in comparison to a QA model trained on full context.Thus, we provide evidence towards a modular approach for question answering that is more robust and interpretable.", "target": ["Un approccio modulare che consiste in un modulo selettore di frasi seguito dal modello di QA può essere reso più robusto agli attacchi avversari rispetto a un modello di QA addestrato sul contesto completo."]} +{"source": "Multi-relational graph embedding which aims at achieving effective representations with reduced low-dimensional parameters, has been widely used in knowledge base completion.Although knowledge base data usually contains tree-like or cyclic structure, none of existing approaches can embed these data into a compatible space that in line with the structure.To overcome this problem, a novel framework, called Riemannian TransE, is proposed in this paper to embed the entities in a Riemannian manifold.Riemannian TransE models each relation as a move to a point and defines specific novel distance dissimilarity for each relation, so that all the relations are naturally embedded in correspondence to the structure of data.Experiments on several knowledge base completion tasks have shown that, based on an appropriate choice of manifold, Riemannian TransE achieves good performance even with a significantly reduced parameters.", "target": ["Embedding di grafi multirelazionali con manifold Riemanniani e loss simile a TransE."]} +{"source": "Catastrophic forgetting in neural networks is one of the most well-known problems in continual learning.Previous attempts on addressing the problem focus on preventing important weights from changing.Such methods often require task boundaries to learn effectively and do not support backward transfer learning.In this paper, we propose a meta-learning algorithm which learns to reconstruct the gradients of old tasks w.r.t. the current parameters and combines these reconstructed gradients with the current gradient to enable continual learning and backward transfer learning from the current task to previous tasks.Experiments on standard continual learning benchmarks show that our algorithm can effectively prevent catastrophic forgetting and supports backward transfer learning.", "target": ["Proponiamo un algoritmo di meta learning per il continual learning che può prevenire efficacemente il problema della catastrophic forgetting e sostenere il backward transfer learning."]} +{"source": "We give a formal procedure for computing preimages of convolutional network outputs using the dual basis defined from the set of hyperplanes associated with the layers of the network.We point out the special symmetry associated with arrangements of hyperplanes of convolutional networks that take the form of regular multidimensional polyhedral cones.We discuss the efficiency of of large number of layers of nested cones that result from incremental small size convolutions in order to give a good compromise between efficient contraction of data to low dimensions and shaping of preimage manifolds.We demonstrate how a specific network flattens a non linear input manifold to an affine output manifold and discuss it's relevance to understanding classification properties of deep networks.", "target": ["Analisi delle deep convolutional network in termini di disposizione associata degli iperpiani"]} +{"source": "Meta learning has been making impressive progress for fast model adaptation.However, limited work has been done on learning fast uncertainty adaption for Bayesian modeling.In this paper, we propose to achieve the goal by placing meta learning on the space of probability measures, inducing the concept of meta sampling for fast uncertainty adaption.Specifically, we propose a Bayesian meta sampling framework consisting of two main components: a meta sampler and a sample adapter.The meta sampler is constructed by adopting a neural-inverse-autoregressive-flow (NIAF) structure, a variant of the recently proposed neural autoregressive flows, to efficiently generate meta samples to be adapted.The sample adapter moves meta samples to task-specific samples, based on a newly proposed and general Bayesian sampling technique, called optimal-transport Bayesian sampling.The combination of the two components allows a simple learning procedure for themeta sampler to be developed, which can be efficiently optimized via standard back-propagation.Extensive experimental results demonstrate the efficiency and effectiveness of the proposed framework, obtaining better sample quality and fasteruncertainty adaption compared to related methods.", "target": ["Abbiamo proposto un metodo di meta sampling bayesiano per adattare l'incertezza del modello nel meta learning"]} +{"source": "We propose a context-adaptive entropy model for use in end-to-end optimized image compression.Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bitallocation is required.Based on these contexts, we allow the model to more accurately estimate the distribution of each latent representation with a more generalized form of the approximation models, which accordingly leads to anenhanced compression performance.Based on the experimental results, the proposed method outperforms the traditional image codecs, such as BPG and JPEG2000, as well as other previous artificial-neural-network (ANN) based approaches, in terms of the peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) index.The test code is publicly available at https://github.com/JooyoungLeeETRI/CA_Entropy_Model.", "target": ["Modello di entropia adattato al contesto da utilizzare nella compressione di immagini ottimizzata end-to-end, che migliora significativamente le prestazioni di compressione"]} +{"source": "Deep networks often perform well on the data distribution on which they are trained, yet give incorrect (and often very confident) answers when evaluated on points from off of the training distribution.This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift. Ideally, a model would assign lower confidence to points unlike those from the training distribution. We propose a regularizer which addresses this issue by training with interpolated hidden states and encouraging the classifier to be less confident at these points. Because the hidden states are learned, this has an important effect of encouraging the hidden states for a class to be concentrated in such a way so that interpolations within the same class or between two different classes do not intersect with the real data points from other classes. This has a major advantage in that it avoids the underfitting which can result from interpolating in the input space. We prove that the exact condition for this problem of underfitting to be avoided by Manifold Mixup is that the dimensionality of the hidden states exceeds the number of classes, which is often the case in practice. Additionally, this concentration can be seen as making the features in earlier layers more discriminative. We show that despite requiring no significant additional computation, Manifold Mixup achieves large improvements over strong baselines in supervised learning, robustness to single-step adversarial attacks, semi-supervised learning, and Negative Log-Likelihood on held out samples.", "target": ["Un metodo per l'apprendimento di rappresentazioni migliori, che agisce come un regolarizzatore e nonostante il suo costo di calcolo aggiuntivo non significativo, raggiunge miglioramenti rispetto a forti baseline su task di apprendimento supervised e semi-supervised."]} +{"source": "Sometimes SRS (Stereotactic Radio Surgery) requires using sphere packing on a Region of Interest (ROI) such as cancer to determine a treatment plan. We have developed a sphere packing algorithm which packs non-intersecting spheres inside the ROI. The region of interest in our case are those voxels which are identified as cancer tissues. In this paper, we analyze the rotational invariant properties of our sphere-packing algorithm which is based on distance transformations.Epsilon-Rotation invariant means the ability to arbitrary rotate the 3D ROI while keeping the volume properties remaining (almost) same within some limit of epsilon.The applied rotations produce spherical packing which remains highly correlated as we analyze the geometrically properties of sphere packing before and after the rotation of the volume data for the ROI.Our novel sphere packing algorithm has high degree of rotation invariance within the range of +/- epsilon.Our method used a shape descriptor derived from the values of the disjoint set of spheres form the distance-based sphere packing algorithm to extract the invariant descriptor from the ROI.We demonstrated by implementing these ideas using Slicer3D platform available for our research. The data is based on sing MRI Stereotactic images.We presented several performance results on different benchmarks data of over 30 patients in Slicer3D platform.", "target": ["Packing delle regioni di interesse (ROI) come le regioni cancerose identificate nei dati di volume 3D, imballaggio delle sfere all'interno della ROI, rotazione della ROI, misure della differenza di packing delle sfere prima e dopo la rotazione."]} +{"source": "We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained using adaptive gradient descent techniques with L2 regularization or weight decay.Through an extensive empirical study (Anonymous, 2019) we hypothesize the mechanism be hind the sparsification process.We find that the interplay of various phenomena influences the strength of L2 and weight decay regularizers, leading the supposedly non sparsity inducing regularizers to induce filter sparsity. In this workshop article we summarize some of our key findings and experiments, and present additional results on modern network architectures such as ResNet-50.", "target": ["La sparsità a livello di filtro emerge implicitamente nelle CNN addestrate con approcci di gradient descent adattivi a causa di vari fenomeni, e l'estensione della sparsità può essere inavvertitamente influenzata da diversi iperparametri apparentemente non correlati."]} +{"source": "Humans can learn a variety of concepts and skills incrementally over the course of their lives while exhibiting an array of desirable properties, such as non-forgetting, concept rehearsal, forward transfer and backward transfer of knowledge, few-shot learning, and selective forgetting.Previous approaches to lifelong machine learning can only demonstrate subsets of these properties, often by combining multiple complex mechanisms. In this Perspective, we propose a powerful unified framework that can demonstrate all of the properties by utilizing a small number of weight consolidation parameters in deep neural networks.In addition, we are able to draw many parallels between the behaviours and mechanisms of our proposed framework and those surrounding human learning, such as memory loss or sleep deprivation.This Perspective serves as a conduit for two-way inspiration to further understand lifelong learning in machines and humans.", "target": ["Facendo un parallelo con l'apprendimento umano, proponiamo un framework unificato per esibire molte capacità di lifelong learning nelle reti neurali utilizzando un piccolo numero di parametri di consolidamento dei pesi."]} +{"source": "Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved.In this work we study how pre-trained generative adversarial networks (GANs) can be used to clean noisy, highly artifact laden reconstructions from conventional techniques, by effectively projecting onto the inferred image manifold.In particular, we use a robust version of the popularly used GAN prior for inverse problems, based on a recent technique called corruption mimicking, that significantly improves the reconstruction quality.The proposed approach operates in the image space directly, as a result of which it does not need to be trained or require access to the measurement model, is scanner agnostic, and can work over a wide range of sensing scenarios.", "target": ["Mostriamo che i prior GAN robusti funzionano meglio dei prior GAN per la ricostruzione CT ad angolo limitato che è un problema inverso altamente sottodeterminato."]} +{"source": "We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision.We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities.We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling.The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FB-NYT dataset.", "target": ["Una nuova forma di attention che funziona bene per setting di distant supervision, e un approccio di multitask learning per aggiungere annotazioni a livello di frase."]} +{"source": "The attention layer in a neural network model provides insights into the model’s reasoning behind its prediction, which are usually criticized for being opaque.Recently, seemingly contradictory viewpoints have emerged about the interpretability of attention weights (Jain & Wallace, 2019; Vig & Belinkov, 2019).Amid such confusion arises the need to understand attention mechanism more systematically.In this work, we attempt to fill this gap by giving a comprehensive explanation which justifies both kinds of observations (i.e., when is attention interpretable and when it is not).Through a series of experiments on diverse NLP tasks, we validate our observations and reinforce our claim of interpretability of attention through manual evaluation.", "target": ["Analisi del meccanismo di attention in diversi task NLP."]} +{"source": "Generative models forsource code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs.We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output.Our model generates code by interleaving grammar-driven expansion steps with graph augmentation and neural message passing steps.An experimental evaluation shows that our new model can generate semantically meaningful expressions, outperforming a range of strong baselines.", "target": ["Rappresentare i programmi come grafi, inclusa la semantica, aiuta a generare programmi"]} +{"source": "The inference of models, prediction of future symbols, and entropy rate estimation of discrete-time, discrete-event processes is well-worn ground.However, many time series are better conceptualized as continuous-time, discrete-event processes.Here, we provide new methods for inferring models, predicting future symbols, and estimating the entropy rate of continuous-time, discrete-event processes.The methods rely on an extension of Bayesian structural inference that takes advantage of neural network’s universal approximation power.Based on experiments with simple synthetic data, these new methods seem to be competitive with state-of- the-art methods for prediction and entropy rate estimation as long as the correct model is inferred.", "target": ["Un nuovo metodo per prevedere processi a tempo continuo ed eventi discreti, dedurne un modello e stimarne il tasso di entropia."]} +{"source": "Recent studies show that convolutional neural networks (CNNs) are vulnerable under various settings, including adversarial examples, backdoor attacks, and distribution shifting. Motivated by the findings that human visual system pays more attention to global structure (e.g., shape) for recognition while CNNs are biased towards local texture features in images, we propose a unified framework EdgeGANRob based on robust edge features to improve the robustness of CNNs in general, which first explicitly extracts shape/structure features from a given image and then reconstructs a new image by refilling the texture information with a trained generative adversarial network (GAN).In addition, to reduce the sensitivity of edge detection algorithm to adversarial perturbation, we propose a robust edge detection approach Robust Canny based on the vanilla Canny algorithm.To gain more insights, we also compare EdgeGANRob with its simplified backbone procedure EdgeNetRob, which performs learning tasks directly on the extracted robust edge features.We find that EdgeNetRob can help boost model robustness significantly but at the cost of the clean model accuracy.EdgeGANRob, on the other hand, is able to improve clean model accuracy compared with EdgeNetRob and without losing the robustness benefits introduced by EdgeNetRob.Extensive experiments show that EdgeGANRob is resilient in different learning tasks under diverse settings.", "target": ["Un modello unificato per migliorare la robustezza del modello contro task multipli"]} +{"source": "Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE).However, by extracting high-level abstractions in the bottom-up inference process, the goal of preserving all factors of variations for top-down generation is compromised.Motivated by the concept of “starting small”, we present a strategy to progressively learn independent hierarchical representations from high- to low-levels of abstractions.The model starts with learning the most abstract representation, and then progressively grow the network architecture to introduce new representations at different levels of abstraction.We quantitatively demonstrate the ability of the presented model to improve disentanglement in comparison to existing works on two benchmark datasets using three disentanglement metrics, including a new metric we proposed to complement the previously-presented metric of mutual information gap.We further present both qualitative and quantitative evidence on how the progression of learning improves disentangling of hierarchical representations.By drawing on the respective advantage of hierarchical representation learning and progressive learning, this is to our knowledge the first attempt to improve disentanglement by progressively growing the capacity of VAE to learn hierarchical representations.", "target": ["Abbiamo proposto un metodo di progressive learning per migliorare l'apprendimento e la separazione delle rappresentazioni latenti a diversi livelli di astrazione."]} +{"source": "Deep latent variable models are powerful tools for representation learning.In this paper, we adopt the deep information bottleneck model, identify its shortcomings and propose a model that circumvents them.To this end, we apply a copula transformation which, by restoring the invariance properties of the information bottleneck method, leads to disentanglement of the features in the latent space.Building on that, we show how this transformation translates to sparsity of the latent space in the new model. We evaluate our method on artificial and real data.", "target": ["Applichiamo la trasformazione della copula al Deep Information Bottleneck che porta a ripristinare le proprietà di invarianza e uno spazio latente separato con capacità predittive superiori."]} +{"source": "State-of-the-art machine learning methods exhibit limited compositional generalization.At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements.We introduce a novel method to systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for creating compositional generalization benchmarks.We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures.We find that they fail to generalize compositionally and that there is a surprisingly strong negative correlation between compound divergence and accuracy.We also demonstrate how our method can be used to create new compositionality benchmarks on top of the existing SCAN dataset, which confirms these findings.", "target": ["Benchmark e metodo per misurare la generalizzazione composizionale massimizzando la divergenza della frequenza dei composti a una piccola divergenza della frequenza degli atomi."]} +{"source": "To understand how object vision develops in infancy and childhood, it will be necessary to develop testable computational models.Deep neural networks (DNNs) have proven valuable as models of adult vision, but it is not yet clear if they have any value as models of development.As a first model, we measured learning in a DNN designed to mimic the architecture and representational geometry of the visual system (CORnet).We quantified the development of explicit object representations at each level of this network through training by freezing the convolutional layers and training an additional linear decoding layer.We evaluate decoding accuracy on the whole ImageNet validation set, and also for individual visual classes.CORnet, however, uses supervised training and because infants have only extremely impoverished access to labels they must instead learn in an unsupervised manner.We therefore also measured learning in a state-of-the-art unsupervised network (DeepCluster).CORnet and DeepCluster differ in both supervision and in the convolutional networks at their heart, thus to isolate the effect of supervision, we ran a control experiment in which we trained the convolutional network from DeepCluster (an AlexNet variant) in a supervised manner.We make predictions on how learning should develop across brain regions in infants.In all three networks, we also tested for a relationship in the order in which infants and machines acquire visual classes, and found only evidence for a counter-intuitive relationship.We discuss the potential reasons for this.", "target": ["Le reti non supervisionate imparano in una maniera bottom-up; le macchine e i bambini acquisiscono classi visive in ordini diversi"]} +{"source": "We show that in a variety of large-scale deep learning scenarios the gradient dynamically converges to a very small subspace after a short period of training.The subspace is spanned by a few top eigenvectors of the Hessian (equal to the number of classes in the dataset), and is mostly preserved over long periods of training.A simple argument then suggests that gradient descent may happen mostly in this subspace.We give an example of this effect in a solvable model of classification, and we comment on possible implications for optimization and learning.", "target": ["Per i problemi di classificazione con k classi, mostriamo che il gradiente tende a stare in un piccolo sottospazio a lenta evoluzione spaziato dagli autovettori corrispondenti ai k più grandi autovalori dell'Hessiano."]} +{"source": "Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question.This paper introduces a new graph-based recurrent retrieval approach that learns to retrieve reasoning paths over the Wikipedia graph to answer multi-hop open-domain questions.Our retriever model trains a recurrent neural network that learns to sequentially retrieve evidence paragraphs in the reasoning path by conditioning on the previously retrieved documents. Our reader model ranks the reasoning paths and extracts the answer span included in the best reasoning path.Experimental results show state-of-the-art results in three open-domain QA datasets, showcasing the effectiveness and robustness of our method.Notably, our method achieves significant improvement in HotpotQA, outperforming the previous best model by more than 14 points.", "target": ["Il retriever ricorrente basato sul grafo che impara a recuperare i percorsi di ragionamento su Wikipedia Graph supera il più recente stato dell'arte su HotpotQA di oltre 14 punti."]} +{"source": "Stereo matching is one of the important basic tasks in the computer vision field.In recent years, stereo matching algorithms based on deep learning have achieved excellent performance and become the mainstream research direction.Existing algorithms generally use deep convolutional neural networks (DCNNs) to extract more abstract semantic information, but we believe that the detailed information of the spatial structure is more important for stereo matching tasks.Based on this point of view, this paper proposes a shallow feature extraction network with a large receptive field.The network consists of three parts: a primary feature extraction module, an atrous spatial pyramid pooling (ASPP) module and a feature fusion module.The primary feature extraction network contains only three convolution layers.This network utilizes the basic feature extraction ability of the shallow network to extract and retain the detailed information of the spatial structure.In this paper, the dilated convolution and atrous spatial pyramid pooling (ASPP) module is introduced to increase the size of receptive field.In addition, a feature fusion module is designed, which integrates the feature maps with multiscale receptive fields and mutually complements the feature information of different scales.We replaced the feature extraction part of the existing stereo matching algorithms with our shallow feature extraction network, and achieved state-of-the-art performance on the KITTI 2015 dataset.Compared with the reference network, the number of parameters is reduced by 42%, and the matching accuracy is improved by 1.9%.", "target": ["Abbiamo introdotto una rete di estrazione di feature poco profonda con un grande campo recettivo per task di stereo matching, che utilizza una framework semplice per ottenere prestazioni migliori."]} +{"source": "We propose a new output layer for deep neural networks that permits the use of logged contextual bandit feedback for training.Such contextual bandit feedback can be available in huge quantities (e.g., logs of search engines, recommender systems) at little cost, opening up a path for training deep networks on orders of magnitude more data.To this effect, we propose a Counterfactual Risk Minimization (CRM) approach for training deep networks using an equivariant empirical risk estimator with variance regularization, BanditNet, and show how the resulting objective can be decomposed in a way that allows Stochastic Gradient Descent (SGD) training.We empirically demonstrate the effectiveness of the method by showing how deep networks -- ResNets in particular -- can be trained for object recognition without conventionally labeled images.", "target": ["L'articolo propone un nuovo layer di uscita per le deep network che permette l'uso del feedback di logged contextual bandit per il training."]} +{"source": "Protein classification is responsible for the biological sequence, we came up with an idea whichdeals with the classification of proteomics using deep learning algorithm.This algorithm focusesmainly to classify sequences of protein-vector which is used for the representation of proteomics.Selection of the type protein representation is challenging based on which output in terms ofaccuracy is depended on, The protein representation used here is n-gram i.e. 3-gram and Kerasembedding used for biological sequences like protein.In this paper we are working on the Proteinclassification to show the strength and representation of biological sequence of the proteins", "target": ["Classificazione delle famiglie di proteine utilizzando deep learning"]} +{"source": "In this work, we attempt to answer a critical question: whether there exists some input sequence that will cause a well-trained discrete-space neural network sequence-to-sequence (seq2seq) model to generate egregious outputs (aggressive, malicious, attacking, etc.).And if such inputs exist, how to find them efficiently.We adopt an empirical methodology, in which we first create lists of egregious output sequences, and then design a discrete optimization algorithm to find input sequences that will cause the model to generate them.Moreover, the optimization algorithm is enhanced for large vocabulary search and constrained to search for input sequences that are likely to be input by real-world users.In our experiments, we apply this approach to dialogue response generation models trained on three real-world dialogue data-sets: Ubuntu, Switchboard and OpenSubtitles, testing whether the model can generate malicious responses.We demonstrate that given the trigger inputs our algorithm finds, a significant number of malicious sentences are assigned large probability by the model, which reveals an undesirable consequence of standard seq2seq training.", "target": ["Questo articolo mira a fornire una risposta empirica alla domanda se un modello di dialogue response ben addestrato possa o meno produrre risposte maliziose."]} +{"source": "In the problem of unsupervised learning of disentangled representations, one of the promising methods is to penalize the total correlation of sampled latent vari-ables. Unfortunately, this well-motivated strategy often fail to achieve disentanglement due to a problematic difference between the sampled latent representation and its corresponding mean representation. We provide a theoretical explanation that low total correlation of sample distribution cannot guarantee low total correlation of the mean representation.We prove that for the mean representation of arbitrarily high total correlation, there exist distributions of latent variables of abounded total correlation. However, we still believe that total correlation could be a key to the disentanglement of unsupervised representative learning, and we propose a remedy, RTC-VAE, which rectifies the total correlation penalty. Experiments show that our model has a more reasonable distribution of the mean representation compared with baseline models, e.g.,β-TCVAE and FactorVAE.", "target": ["Viene diagnosticato tutto il problema di STOA VAEs teoricamente e qualitativamente"]} +{"source": "Visual attention mechanisms have been widely used in image captioning models.In this paper, to better link the image structure with the generated text, we replace the traditional softmax attention mechanism by two alternative sparsity-promoting transformations: sparsemax and Total-Variation Sparse Attention (TVmax).With sparsemax, we obtain sparse attention weights, selecting relevant features. In order to promote sparsity and encourage fusing of the related adjacent spatial locations, we propose TVmax. By selecting relevant groups of features, the TVmax transformation improves interpretability.We present results in the Microsoft COCO and Flickr30k datasets, obtaining gains in comparison to softmax. TVmax outperforms the other compared attention mechanisms in terms of human-rated caption quality and attention relevance.", "target": ["Proponiamo un nuovo meccanismo di attention sparsa e strutturata, TVmax, che promuove la sparsità e incoraggia il peso delle posizioni adiacenti correlate ad essere lo stesso."]} +{"source": "Although there are more than 65,000 languages in the world, the pronunciations of many phonemes sound similar across the languages.When people learn a foreign language, their pronunciation often reflect their native language's characteristics.That motivates us to investigate how the speech synthesis network learns the pronunciation when multi-lingual dataset is given.In this study, we train the speech synthesis network bilingually in English and Korean, and analyze how the network learns the relations of phoneme pronunciation between the languages.Our experimental result shows that the learned phoneme embedding vectors are located closer if their pronunciations are similar across the languages.Based on the result, we also show that it is possible to train networks that synthesize English speaker's Korean speech and vice versa.In another experiment, we train the network with limited amount of English dataset and large Korean dataset, and analyze the required amount of dataset to train a resource-poor language with the help of resource-rich languages.", "target": ["Gli embedding dei fonemi appresi dalla rete di sintesi vocale neurale e multilingue potrebbero rappresentare le relazioni di pronuncia dei fonemi tra le lingue."]} +{"source": "Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability.However, visualization and understanding of GANs is largely missing.How does a GAN represent our visual world internally?What causes the artifacts in GAN results?How do architectural choices affect GAN learning?Answering such questions could enable us to develop new insights and better models.In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level.We first identify a group of interpretable units that are closely related to object concepts with a segmentation-based network dissection method.Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output.Finally, we examine the contextual relationship between these units and their surrounding by inserting the discovered object concepts into new images.We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in the scene.We provide open source interpretation tools to help peer researchers and practitioners better understand their GAN models.", "target": ["Le rappresentazioni GAN sono esaminate in dettaglio, e si trovano insiemi di unità di rappresentazione che controllano la generazione di concetti semantici nell'output."]} +{"source": "Historically, the pursuit of efficient inference has been one of the driving forces be-hind the research into new deep learning architectures and building blocks.Some of the recent examples include: the squeeze-and-excitation module of (Hu et al.,2018), depthwise separable convolutions in Xception (Chollet, 2017), and the inverted bottleneck in MobileNet v2 (Sandler et al., 2018). Notably, in all of these cases, the resulting building blocks enabled not only higher efficiency, but also higher accuracy, and found wide adoption in the field.In this work, we further expand the arsenal of efficient building blocks for neural network architectures; but instead of combining standard primitives (such as convolution), we advocate for the replacement of these dense primitives with their sparse counterparts. While the idea of using sparsity to decrease the parameter count is not new (Mozer & Smolensky, 1989), the conventional wisdom is that this reduction in theoretical FLOPs does not translate into real-world efficiency gains. We aim to correct this misconception by introducing a family of efficient sparse kernels for several hardware platforms, which we plan to open-source for the benefit of the community.Equipped with our efficient implementation of sparse primitives, we show that sparse versions of MobileNet v1 and MobileNet v2 architectures substantially outperform strong dense baselines on the efficiency-accuracy curve. On Snapdragon 835 our sparse networks outperform their dense equivalents by 1.1−2.2x – equivalent to approximately one entire generation of improvement. We hope that our findings will facilitate wider adoption of sparsity as a tool for creating efficient and accurate deep learning architectures.", "target": ["Le reti mobili sparse sono più veloci di quelle dense con i kernel appropriati."]} +{"source": "In this work, we address the semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures.Recent works often solve this problem with the advanced graph convolution in a conventional supervised manner, but the performance could be heavily affected when labeled data is scarce.Here we propose a Graph Inference Learning (GIL) framework to boost the performance of node classification by learning the inference of node labels on graph topology.To bridge the connection of two nodes, we formally define a structure relation by encapsulating node attributes, between-node paths and local topological structures together, which can make inference conveniently deduced from one node to another node.For learning the inference process, we further introduce meta-optimization on structure relations from training nodes to validation nodes, such that the learnt graph inference capability can be better self-adapted into test nodes.Comprehensive evaluations on four benchmark datasets (including Cora, Citeseer, Pubmed and NELL) demonstrate the superiority of our GIL when compared with other state-of-the-art methods in the semi-supervised node classification task.", "target": ["Proponiamo un nuovo framework di apprendimento dell'inferenza dei grafi costruendo relazioni di struttura per dedurre label di nodi sconosciuti da quelli annotati in un modo end-to-end."]} +{"source": "This paper proposes a method for efficient training of Q-function for continuous-state Markov Decision Processes (MDP), such that the traces of the resulting policies satisfy a Linear Temporal Logic (LTL) property.LTL, a modal logic, can express a wide range of time-dependent logical properties including safety and liveness.We convert the LTL property into a limit deterministic Buchi automaton with which a synchronized product MDP is constructed.The control policy is then synthesised by a reinforcement learning algorithm assuming that no prior knowledge is available from the MDP.The proposed method is evaluated in a numerical study to test the quality of the generated control policy and is compared against conventional methods for policy synthesis such as MDP abstraction (Voronoi quantizer) and approximate dynamic programming (fitted value iteration).", "target": ["Poiché la sicurezza sta diventando una nozione critica nell'apprendimento automatico, crediamo che questo lavoro possa fungere da base per una serie di direzioni di ricerca come gli algoritmi di apprendimento consapevoli della sicurezza."]} +{"source": "We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i.e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.The resulting class of simulators are used extensively throughout the sciences and can be interpreted as probabilistic generative models.However, drawing inferences from them poses a substantial challenge due to the inability to evaluate even their unnormalised density, preventing the use of many standard inference procedures like Markov Chain Monte Carlo (MCMC).To address this, we introduce inference algorithms based on a two-step approach that first approximates the conditional densities of the individual sub-procedures, before using these approximations to run MCMC methods on the full program.Because the sub-procedures can be dealt with separately and are lower-dimensional than that of the overall problem, this two-step process allows them to be isolated and thus be tractably dealt with, without placing restrictions on the overall dimensionality of the problem.We demonstrate the utility of our approach on a simple, artificially constructed simulator.", "target": ["Introduciamo due approcci per un'inferenza efficiente e scalabile nei simulatori stocastici per i quali la densità non può essere valutata direttamente a causa, per esempio, di cicli di rejection sampling."]} +{"source": "While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary).Previous work has studied this tradeoff between standard and robust accuracy, but only in the setting where no predictor performs well on both objectives in the infinite data limit.In this paper, we show that even when the optimal predictor with infinite data performs well on both objectives, a tradeoff can still manifest itself with finite data.Furthermore, since our construction is based on a convex learning problem, we rule out optimization concerns, thus laying bare a fundamental tension between robustness and generalization.Finally, we show that robust self-training mostly eliminates this tradeoff by leveraging unlabeled data.", "target": ["Anche se non c'è compromesso nel limite infinito dei dati, l'adversarial training può avere un'accuratezza standard peggiore anche in un problema convesso."]} +{"source": "Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency.In particular, the recent DenseNet is efficient in computation and parameters, and achieves state-of-the-art predictions by directly connecting each feature layer to all previous ones.However, DenseNet's extreme connectivity pattern may hinder its scalability to high depths, and in applications like fully convolutional networks, full DenseNet connections are prohibitively expensive. This work first experimentally shows that one key advantage of skip connections is to have short distances among feature layers during backpropagation.Specifically, using a fixed number of skip connections, the connection patterns with shorter backpropagation distance among layers have more accurate predictions.Following this insight, we propose a connection template, Log-DenseNet, which, in comparison to DenseNet, only slightly increases the backpropagation distances among layers from 1 to ($1 + \\log_2 L$), but uses only $L\\log_2 L$ total connections instead of $O(L^2)$.Hence, \\logdenses are easier to scale than DenseNets, and no longer require careful GPU memory management.We demonstrate the effectiveness of our design principle by showing better performance than DenseNets on tabula rasa semantic segmentation, and competitive results on visual recognition.", "target": ["Mostriamo che le shortcut connection dovrebbero essere posizionate in pattern che minimizzano le distanze tra i layer durante la backpropagation, e progettiamo reti che raggiungono distanze log L usando L log(L) connessioni."]} +{"source": "Building agents to interact with the web would allow for significant improvements in knowledge understanding and representation learning.However, web navigation tasks are difficult for current deep reinforcement learning (RL) models due to the large discrete action space and the varying number of actions between the states.In this work, we introduce DOM-Q-NET, a novel architecture for RL-based web navigation to address both of these problems.It parametrizes Q functions with separate networks for different action categories: clicking a DOM element and typing a string input. Our model utilizes a graph neural network to represent the tree-structured HTML of a standard web page. We demonstrate the capabilities of our model on the MiniWoB environment where we can match or outperform existing work without the use of expert demonstrations.Furthermore, we show 2x improvements in sample efficiency when training in the multi-task setting, allowing our model to transfer learned behaviours across tasks.", "target": ["Deep Q Network basata su grafi per la navigazione web"]} +{"source": "Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning.Recently, concerns about the viability of learning disentangled representations in a purely unsupervised manner has spurred a shift toward the incorporation of weak supervision.However, there is currently no formalism that identifies when and how weak supervision will guarantee disentanglement.To address this issue, we provide a theoretical framework—including a calculus of disentanglement— to assist in analyzing the disentanglement guarantees (or lack thereof) conferred by weak supervision when coupled with learning algorithms based on distribution matching.We empirically verify the guarantees and limitations of several weak supervision methods (restricted labeling, match-pairing, and rank-pairing), demonstrating the predictive power and usefulness of our theoretical framework.", "target": ["Abbiamo costruito un framework teorico per il disentanglement weakly supervised e abbiamo condotto molti esperimenti per sostenere la teoria."]} +{"source": "Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable, and task boundaries are known during training.However, if our goal is to develop an algorithm that learns as humans do, this setting is far from realistic, and it is essential to develop a methodology that works in a task-free manner.Meanwhile, among several branches of continual learning, expansion-based methods have the advantage of eliminating catastrophic forgetting by allocating new resources to learn new data.In this work, we propose an expansion-based approach for task-free continual learning.Our model, named Continual Neural Dirichlet Process Mixture (CN-DPM), consists of a set of neural network experts that are in charge of a subset of the data.CN-DPM expands the number of experts in a principled way under the Bayesian nonparametric framework.With extensive experiments, we show that our model successfully performs task-free continual learning for both discriminative and generative tasks such as image classification and image generation.", "target": ["Proponiamo per la prima volta un approccio basato sull'espansione per il continual learning senza task. Il nostro modello consiste in un insieme di esperti basati su reti neurali ed espande il numero di esperti secondo il principio Bayesiano nonparametrico."]} +{"source": "Stochastic Gradient Descent (SGD) with Nesterov's momentum is a widely used optimizer in deep learning, which is observed to have excellent generalization performance.However, due to the large stochasticity, SGD with Nesterov's momentum is not robust, i.e., its performance may deviate significantly from the expectation.In this work, we propose Amortized Nesterov's Momentum, a special variant of Nesterov's momentum which has more robust iterates, faster convergence in the early stage and higher efficiency.Our experimental results show that this new momentum achieves similar (sometimes better) generalization performance with little-to-no tuning.In the convex case, we provide optimal convergence rates for our new methods and discuss how the theorems explain the empirical results.", "target": ["Ammortizzare il momentum di Nesterov per un training di deep learning più robusto, leggero e veloce."]} +{"source": "A state-of-the-art generative model, a ”factorized action variational autoencoder (FAVAE),” is presented for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision.The purpose of disentangled representation learning is to obtain interpretable and transferable representations from data.We focused on the disentangled representation of sequential data because there is a wide range of potential applications if disentanglement representation is extended to sequential data such as video, speech, and stock price data.Sequential data is characterized by dynamic factors and static factors: dynamic factors are time-dependent, and static factors are independent of time.Previous works succeed in disentangling static factors and dynamic factors by explicitly modeling the priors of latent variables to distinguish between static and dynamic factors.However, this model can not disentangle representations between dynamic factors, such as disentangling ”picking” and ”throwing” in robotic tasks.In this paper, we propose new model that can disentangle multiple dynamic factors.Since our method does not require modeling priors, it is capable of disentangling ”between” dynamic factors.In experiments, we show that FAVAE can extract the disentangled dynamic factors.", "target": ["Proponiamo un nuovo modello che può distinguere più fattori dinamici in dati sequenziali"]} +{"source": "Generative networks are promising models for specifying visual transformations.Unfortunately, certification of generative models is challenging as one needs to capture sufficient non-convexity so to produce precise bounds on the output.Existing verification methods either fail to scale to generative networks or do not capture enough non-convexity.In this work, we present a new verifier, called ApproxLine, that can certify non-trivial properties of generative networks.ApproxLine performs both deterministic and probabilistic abstract interpretation and captures infinite sets of outputs of generative networks.We show that ApproxLine can verify interesting interpolations in the network's latent space.", "target": ["Verifichiamo le proprietà deterministiche e probabilistiche delle reti neurali usando rilassamenti non convessi su trasformazioni visibili specificate da modelli generativi"]} +{"source": "Multi-view video summarization (MVS) lacks researchers’ attention due to their major challenges of inter-view correlations and overlapping of cameras.Most of the prior MVS works are offline, relying on only summary, needing extra communication bandwidth and transmission time with no focus on uncertain environments.Different from the existing methods, we propose edge intelligence based MVS and spatio-temporal features based activity recognition for IoT environments.We segment the multi-view videos on each slave device over edge into shots using light-weight CNN object detection model and compute mutual information among them to generate summary.Our system does not rely on summary only but encode and transmit it to a master device with neural computing stick (NCS) for intelligently computing inter-view correlations and efficiently recognizing activities, thereby saving computation resources, communication bandwidth, and transmission time.Experiments report an increase of 0.4 in F-measure score on MVS Office dataset as well as 0.2% and 2% increase in activity recognition accuracy over UCF-50 and YouTube 11 datasets, respectively, with lower storage and transmission time compared to state-of-the-art.The time complexity is decreased from 1.23 to 0.45 secs for a single frame processing, thereby generating 0.75 secs faster MVS.Furthermore, we made a new dataset by synthetically adding fog to an MVS dataset to show the adaptability of our system for both certain and uncertain surveillance environments.", "target": ["Un efficiente schema di summarization video multi-view avanzato per il riconoscimento delle attività in ambienti IoT."]} +{"source": "A central goal in the study of the primate visual cortex and hierarchical models for object recognition is understanding how and why single units trade off invariance versus sensitivity to image transformations.For example, in both deep networks and visual cortex there is substantial variation from layer-to-layer and unit-to-unit in the degree of translation invariance.Here, we provide theoretical insight into this variation and its consequences for encoding in a deep network.Our critical insight comes from the fact that rectification simultaneously decreases response variance and correlation across responses to transformed stimuli, naturally inducing a positive relationship between invariance and dynamic range.Invariant input units then tend to drive the network more than those sensitive to small image transformations.We discuss consequences of this relationship for AI: deep nets naturally weight invariant units over sensitive units, and this can be strengthened with training, perhaps contributing to generalization performance.Our results predict a signature relationship between invariance and dynamic range that can now be tested in future neurophysiological studies.", "target": ["La presenza di ReLU nelle deep neural network le porta naturalmente a favorire una rappresentazione invariante."]} +{"source": "We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment. This end-to-end learning method for audio source separation operates directly in the time domain, permitting the integrated modelling of phase information and being able to take large temporal contexts into account. Our experiments show that the proposed method improves several metrics, namely PESQ, CSIG, CBAK, COVL and SSNR, over the state-of-the-art with respect to the speech enhancement task on the Voice Bank corpus (VCTK) dataset.We find that a reduced number of hidden layers is sufficient for speech enhancement in comparison to the original system designed for singing voice separation in music.We see this initial result as an encouraging signal to further explore speech enhancement in the time-domain, both as an end in itself and as a pre-processing step to speech recognition systems.", "target": ["L'architettura Wave-U-Net, recentemente introdotta da Stoller et al per music source separation, è altamente efficace per lo speech enhancement, battendo lo stato dell'arte."]} +{"source": "Reinforcement learning (RL) is a powerful framework for solving problems by exploring and learning from mistakes.However, in the context of autonomous vehicle (AV) control, requiring an agent to make mistakes, or even allowing mistakes, can be quite dangerous and costly in the real world.For this reason, AV RL is generally only viable in simulation.Because these simulations have imperfect representations, particularly with respect to graphics, physics, and human interaction, we find motivation for a framework similar to RL, suitable to the real world.To this end, we formulate a learning framework that learns from restricted exploration by having a human demonstrator do the exploration.Existing work on learning from demonstration typically either assumes the collected data is performed by an optimal expert, or requires potentially dangerous exploration to find the optimal policy.We propose an alternative framework that learns continuous control from only safe behavior.One of our key insights is that the problem becomes tractable if the feedback score that rates the demonstration applies to the atomic action, as opposed to the entire sequence of actions.We use human experts to collect driving data as well as to label the driving data through a framework we call ``Backseat Driver'', giving us state-action pairs matched with scalar values representing the score for the action.We call the more general learning framework ReNeg, since it learns a regression from states to actions given negative as well as positive examples.We empirically validate several models in the ReNeg framework, testing on lane-following with limited data.We find that the best solution in this context outperforms behavioral cloning has strong connections to stochastic policy gradient approaches.", "target": ["Introduciamo un nuovo framework per l'apprendimento dalla dimostrazione che utilizza il feedback umano continuo; valutiamo questo framework sul controllo continuo per i veicoli autonomi."]} +{"source": "We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation.To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, thus our model is an attractive candidate for applications where the encoding and decoding speed is critical.Additionally, this allows us to only sample autoregressively in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images.We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.", "target": ["Scalare e migliorare VQ-VAE con prior potenti per generare immagini quasi realistiche."]} +{"source": "We present a graph neural network assisted Monte Carlo Tree Search approach for the classical traveling salesman problem (TSP).We adopt a greedy algorithm framework to construct the optimal solution to TSP by adding the nodes successively.A graph neural network (GNN) is trained to capture the local and global graph structure and give the prior probability of selecting each vertex every step.The prior probability provides a heuristics for MCTS, and the MCTS output is an improved probability for selecting the successive vertex, as it is the feedback information by fusing the prior with the scouting procedure.Experimental results on TSP up to 100 nodes demonstrate that the proposed method obtains shorter tours than other learning-based methods.", "target": ["Un approccio di Monte Carlo Tree Search assistito da graph neural network al Traveling Salesman Problem"]} +{"source": "Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules.These models represent a molecule as a graph using only the distance between atoms (nodes) and not the spatial direction from one atom to another.However, directional information plays a central role in empirical potentials for molecules, e.g. in angular potentials.To alleviate this limitation we propose directional message passing, in which we embed the messages passed between atoms instead of the atoms themselves.Each message is associated with a direction in coordinate space.These directional message embeddings are rotationally equivariant since the associated directions rotate with the molecule.We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them.Additionally, we use spherical Bessel functions to construct a theoretically well-founded, orthogonal radial basis that achieves better performance than the currently prevalent Gaussian radial basis functions while using more than 4x fewer parameters.We leverage these innovations to construct the directional message passing neural network (DimeNet).DimeNet outperforms previous GNNs on average by 77% on MD17 and by 41% on QM9.", "target": ["Il passaggio direzionale dei messaggi incorpora informazioni direzionali spaziali per migliorare le graph neural network."]} +{"source": "Training an agent to solve control tasks directly from high-dimensional images with model-free reinforcement learning (RL) has proven difficult.The agent needs to learn a latent representation together with a control policy to perform the task.Fitting a high-capacity encoder using a scarce reward signal is not only extremely sample inefficient, but also prone to suboptimal convergence.Two ways to improve sample efficiency are to learn a good feature representation and use off-policy algorithms.We dissect various approaches of learning good latent features, and conclude that the image reconstruction loss is the essential ingredient that enables efficient and stable representation learning in image-based RL.Following these findings, we devise an off-policy actor-critic algorithm with an auxiliary decoder that trains end-to-end and matches state-of-the-art performance across both model-free and model-based algorithms on many challenging control tasks.We release our code to encourage future research on image-based RL.", "target": ["Progettiamo un metodo off-policy semplice ed efficiente senza modello per il reinforcement learning basato sulle immagini che eguaglia i metodi basati sul modello allo stato dell'arte in sample efficiency"]} +{"source": "Large deep neural networks require huge memory to run and their running speed is sometimes too slow for real applications.Therefore network size reduction with keeping accuracy is crucial for practical applications.We present a novel neural network operator, chopout, with which neural networks are trained, even in a single training process, so as to truncated sub-networks perform as well as possible.Chopout is easy to implement and integrate into most type of existing neural networks.Furthermore it enables to reduce size of networks and latent representations even after training just by truncating layers.We show its effectiveness through several experiments.", "target": ["Presentiamo un nuovo operatore semplice, chopout, con il quale le reti neurali sono addestrate, anche in un unico processo di training, in modo che le sottoreti troncate possano ottenere le migliori prestazioni possibili."]} +{"source": "Generative Adversarial Networks (GANs) have been shown to produce realistically looking synthetic images with remarkable success, yet their performance seems less impressive when the training set is highly diverse.In order to provide a better fit to the target data distribution when the dataset includes many different classes, we propose a variant of the basic GAN model, a Multi-Modal Gaussian-Mixture GAN (GM-GAN), where the probability distribution over the latent space is a mixture of Gaussians.We also propose a supervised variant which is capable of conditional sample synthesis.In order to evaluate the model's performance, we propose a new scoring method which separately takes into account two (typically conflicting) measures - diversity vs. quality of the generated data. Through a series of experiments, using both synthetic and real-world datasets, we quantitatively show that GM-GANs outperform baselines, both when evaluated using the commonly used Inception Score, and when evaluated using our own alternative scoring method.In addition, we qualitatively demonstrate how the unsupervised variant of GM-GAN tends to map latent vectors sampled from different Gaussians in the latent space to samples of different classes in the data space.We show how this phenomenon can be exploited for the task of unsupervised clustering, and provide quantitative evaluation showing the superiority of our method for the unsupervised clustering of image datasets.Finally, we demonstrate a feature which further sets our model apart from other GAN models: the option to control the quality-diversity trade-off by altering, post-training, the probability distribution of the latent space.This allows one to sample higher quality and lower diversity samples, or vice versa, according to one's needs.", "target": ["La distribuzione gaussiana multimodale dello spazio latente nei modelli GAN migliora le prestazioni e permette un trade-off tra qualità e diversità"]} +{"source": "Distributed optimization is essential for training large models on large datasets.Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local SGD steps, and decentralized methods (e.g., using gossip algorithms) to decouple communications among workers.Although these methods run faster than AllReduce-based methods, which use blocking communication before every update, the resulting models may be less accurate after the same number of updates.Inspired by the BMUF method of Chen & Huo (2016), we propose a slow momentum (SloMo) framework, where workers periodically synchronize and perform a momentum update, after multiple iterations of a base optimization algorithm.Experiments on image classification and machine translation tasks demonstrate that SloMo consistently yields improvements in optimization and generalization performance relative to the base optimizer, even when the additional overhead is amortized over many updates so that the SloMo runtime is on par with that of the base optimizer.We provide theoretical convergence guarantees showing that SloMo converges to a stationary point of smooth non-convex losses.Since BMUF is a particular instance of the SloMo framework, our results also correspond to the first theoretical convergence guarantees for BMUF.", "target": ["SlowMo migliora le prestazioni di ottimizzazione e generalizzazione degli algoritmi decentralizzati efficienti dal punto di vista della comunicazione senza sacrificare la velocità."]} +{"source": "Structural planning is important for producing long sentences, which is a missing part in current language generation models.In this work, we add a planning phase in neural machine translation to control the coarse structure of output sentences.The model first generates some planner codes, then predicts real output words conditioned on them.The codes are learned to capture the coarse structure of the target sentence.In order to learn the codes, we design an end-to-end neural network with a discretization bottleneck, which predicts the simplified part-of-speech tags of target sentences.Experiments show that the translation performance are generally improved by planning ahead.We also find that translations with different structures can be obtained by manipulating the planner codes.", "target": ["Pianificare la struttura sintattica della traduzione usando i codici"]} +{"source": "Interpolation of data in deep neural networks has become a subject of significant research interest. We prove that over-parameterized single layer fully connected autoencoders do not merely interpolate, but rather, memorize training data: they produce outputs in (a non-linear version of) the span of the training examples.In contrast to fully connected autoencoders, we prove that depth is necessary for memorization in convolutional autoencoders. Moreover, we observe that adding nonlinearity to deep convolutional autoencoders results in a stronger form of memorization: instead of outputting points in the span of the training images, deep convolutional autoencoders tend to output individual training images. Since convolutional autoencoder components are building blocks of deep convolutional networks, we envision that our findings will shed light on the important question of the inductive bias in over-parameterized deep networks.", "target": ["Identifichiamo la memorizzazione come il bias induttivo dell'interpolazione in autoencoder fully connected e convoluzionali iperparametrizzati."]} +{"source": "The tremendous success of deep neural networks has motivated the need to better understand the fundamental properties of these networks, but many of the theoretical results proposed have only been for shallow networks.In this paper, we study an important primitive for understanding the meaningful input space of a deep network: span recovery.For $k