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['Ahmed Osman', 'Wojciech Samek']

1802.00209v1

We propose an architecture for VQA which utilizes recurrent layers to generate visual and textual attention. The memory characteristic of the proposed recurrent attention units offers a rich joint embedding of visual and textual features and enables the model to reason relations between several parts of the image and question. Our single model outperforms the first place winner on the VQA 1.0 dataset, performs within margin to the current state-of-the-art ensemble model. We also experiment with replacing attention mechanisms in other state-of-the-art models with our implementation and show increased accuracy. In both cases, our recurrent attention mechanism improves performance in tasks requiring sequential or relational reasoning on the VQA dataset.

Dual Recurrent Attention Units for Visual Question Answering

http://arxiv.org/pdf/1802.00209v1

Title Dual Recurrent Attention Units Visual Question Answering Summary propose architecture VQA utilizes recurrent layer generate visual textual attention memory characteristic proposed recurrent attention unit offer rich joint embedding visual textual feature enables model reason relation several part image question single model outperforms first place winner VQA 10 dataset performs within margin current stateoftheart ensemble model also experiment replacing attention mechanism stateoftheart model implementation show increased accuracy case recurrent attention mechanism improves performance task requiring sequential relational reasoning VQA dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ji Young Lee', 'Franck Dernoncourt']

1603.03827v1

Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.

Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

http://arxiv.org/pdf/1603.03827v1

Title Sequential ShortText Classification Recurrent Convolutional Neural Networks Summary Recent approach based artificial neural network ANNs shown promising result shorttext classification However many short text occur sequence eg sentence document utterance dialog existing ANNbased system leverage preceding short text classifying subsequent one work present model based recurrent neural network convolutional neural network incorporates preceding short text model achieves stateoftheart result three different datasets dialog act prediction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian Vlad Serban', 'Tim Klinger', 'Gerald Tesauro', 'Kartik Talamadupula', 'Bowen Zhou', 'Yoshua Bengio', 'Aaron Courville']

1606.00776v2

We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the high-level coarse tokens, but we argue that a simple extraction procedure is sufficient to capture a wealth of high-level discourse semantics. Such procedure allows training the multiresolution recurrent neural network by maximizing the exact joint log-likelihood over both sequences. In contrast to the standard log- likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions. We apply the proposed model to the task of dialogue response generation in two challenging domains: the Ubuntu technical support domain, and Twitter conversations. On Ubuntu, the model outperforms competing approaches by a substantial margin, achieving state-of-the-art results according to both automatic evaluation metrics and a human evaluation study. On Twitter, the model appears to generate more relevant and on-topic responses according to automatic evaluation metrics. Finally, our experiments demonstrate that the proposed model is more adept at overcoming the sparsity of natural language and is better able to capture long-term structure.

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

http://arxiv.org/pdf/1606.00776v2

Title Multiresolution Recurrent Neural Networks Application Dialogue Response Generation Summary introduce multiresolution recurrent neural network extends sequencetosequence framework model natural language generation two parallel discrete stochastic process sequence highlevel coarse token sequence natural language token many way estimate learn highlevel coarse token argue simple extraction procedure sufficient capture wealth highlevel discourse semantics procedure allows training multiresolution recurrent neural network maximizing exact joint loglikelihood sequence contrast standard log likelihood objective wrt natural language token word perplexity optimizing joint loglikelihood bias model towards modeling highlevel abstraction apply proposed model task dialogue response generation two challenging domain Ubuntu technical support domain Twitter conversation Ubuntu model outperforms competing approach substantial margin achieving stateoftheart result according automatic evaluation metric human evaluation study Twitter model appears generate relevant ontopic response according automatic evaluation metric Finally experiment demonstrate proposed model adept overcoming sparsity natural language better able capture longterm structure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sebastian Ruder', 'Joachim Bingel', 'Isabelle Augenstein', 'Anders Søgaard']

1705.08142v2

Multi-task learning is motivated by the observation that humans bring to bear what they know about related problems when solving new ones. Similarly, deep neural networks can profit from related tasks by sharing parameters with other networks. However, humans do not consciously decide to transfer knowledge between tasks. In Natural Language Processing (NLP), it is hard to predict if sharing will lead to improvements, particularly if tasks are only loosely related. To overcome this, we introduce Sluice Networks, a general framework for multi-task learning where trainable parameters control the amount of sharing. Our framework generalizes previous proposals in enabling sharing of all combinations of subspaces, layers, and skip connections. We perform experiments on three task pairs, and across seven different domains, using data from OntoNotes 5.0, and achieve up to 15% average error reductions over common approaches to multi-task learning. We show that a) label entropy is predictive of gains in sluice networks, confirming findings for hard parameter sharing and b) while sluice networks easily fit noise, they are robust across domains in practice.

Learning what to share between loosely related tasks

http://arxiv.org/pdf/1705.08142v2

Title Learning share loosely related task Summary Multitask learning motivated observation human bring bear know related problem solving new one Similarly deep neural network profit related task sharing parameter network However human consciously decide transfer knowledge task Natural Language Processing NLP hard predict sharing lead improvement particularly task loosely related overcome introduce Sluice Networks general framework multitask learning trainable parameter control amount sharing framework generalizes previous proposal enabling sharing combination subspace layer skip connection perform experiment three task pair across seven different domain using data OntoNotes 50 achieve 15 average error reduction common approach multitask learning show label entropy predictive gain sluice network confirming finding hard parameter sharing b sluice network easily fit noise robust across domain practice Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian V. Serban', 'Chinnadhurai Sankar', 'Mathieu Germain', 'Saizheng Zhang', 'Zhouhan Lin', 'Sandeep Subramanian', 'Taesup Kim', 'Michael Pieper', 'Sarath Chandar', 'Nan Rosemary Ke', 'Sai Rajeshwar', 'Alexandre de Brebisson', 'Jose M. R. Sotelo', 'Dendi Suhubdy', 'Vincent Michalski', 'Alexandre Nguyen', 'Joelle Pineau', 'Yoshua Bengio']

1709.02349v2

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including template-based models, bag-of-words models, sequence-to-sequence neural network and latent variable neural network models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than many competing systems. Due to its machine learning architecture, the system is likely to improve with additional data.

A Deep Reinforcement Learning Chatbot

http://arxiv.org/pdf/1709.02349v2

Title Deep Reinforcement Learning Chatbot Summary present MILABOT deep reinforcement learning chatbot developed Montreal Institute Learning Algorithms MILA Amazon Alexa Prize competition MILABOT capable conversing human popular small talk topic speech text system consists ensemble natural language generation retrieval model including templatebased model bagofwords model sequencetosequence neural network latent variable neural network model applying reinforcement learning crowdsourced data realworld user interaction system trained select appropriate response model ensemble system evaluated AB testing realworld user performed significantly better many competing system Due machine learning architecture system likely improve additional data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kelvin Guu', 'Tatsunori B. Hashimoto', 'Yonatan Oren', 'Percy Liang']

1709.08878v1

We propose a new generative model of sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional models that generate from scratch either left-to-right or by first sampling a latent sentence vector, our prototype-then-edit model improves perplexity on language modeling and generates higher quality outputs according to human evaluation. Furthermore, the model gives rise to a latent edit vector that captures interpretable semantics such as sentence similarity and sentence-level analogies.

Generating Sentences by Editing Prototypes

http://arxiv.org/pdf/1709.08878v1

Title Generating Sentences Editing Prototypes Summary propose new generative model sentence first sample prototype sentence training corpus edits new sentence Compared traditional model generate scratch either lefttoright first sampling latent sentence vector prototypethenedit model improves perplexity language modeling generates higher quality output according human evaluation Furthermore model give rise latent edit vector capture interpretable semantics sentence similarity sentencelevel analogy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian V. Serban', 'Chinnadhurai Sankar', 'Mathieu Germain', 'Saizheng Zhang', 'Zhouhan Lin', 'Sandeep Subramanian', 'Taesup Kim', 'Michael Pieper', 'Sarath Chandar', 'Nan Rosemary Ke', 'Sai Rajeswar', 'Alexandre de Brebisson', 'Jose M. R. Sotelo', 'Dendi Suhubdy', 'Vincent Michalski', 'Alexandre Nguyen', 'Joelle Pineau', 'Yoshua Bengio']

1801.06700v1

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including neural network and template-based models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than other systems. The results highlight the potential of coupling ensemble systems with deep reinforcement learning as a fruitful path for developing real-world, open-domain conversational agents.

A Deep Reinforcement Learning Chatbot (Short Version)

http://arxiv.org/pdf/1801.06700v1

Title Deep Reinforcement Learning Chatbot Short Version Summary present MILABOT deep reinforcement learning chatbot developed Montreal Institute Learning Algorithms MILA Amazon Alexa Prize competition MILABOT capable conversing human popular small talk topic speech text system consists ensemble natural language generation retrieval model including neural network templatebased model applying reinforcement learning crowdsourced data realworld user interaction system trained select appropriate response model ensemble system evaluated AB testing realworld user performed significantly better system result highlight potential coupling ensemble system deep reinforcement learning fruitful path developing realworld opendomain conversational agent Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Darko Brodic', 'Alessia Amelio', 'Zoran N. Milivojevic', 'Milena Jevtic']

1609.06492v1

The paper introduces a new method for discrimination of documents given in different scripts. The document is mapped into a uniformly coded text of numerical values. It is derived from the position of the letters in the text line, based on their typographical characteristics. Each code is considered as a gray level. Accordingly, the coded text determines a 1-D image, on which texture analysis by run-length statistics and local binary pattern is performed. It defines feature vectors representing the script content of the document. A modified clustering approach employed on document feature vector groups documents written in the same script. Experimentation performed on two custom oriented databases of historical documents in old Cyrillic, angular and round Glagolitic as well as Antiqua and Fraktur scripts demonstrates the superiority of the proposed method with respect to well-known methods in the state-of-the-art.

Document Image Coding and Clustering for Script Discrimination

http://arxiv.org/pdf/1609.06492v1

Title Document Image Coding Clustering Script Discrimination Summary paper introduces new method discrimination document given different script document mapped uniformly coded text numerical value derived position letter text line based typographical characteristic code considered gray level Accordingly coded text determines 1D image texture analysis runlength statistic local binary pattern performed defines feature vector representing script content document modified clustering approach employed document feature vector group document written script Experimentation performed two custom oriented database historical document old Cyrillic angular round Glagolitic well Antiqua Fraktur script demonstrates superiority proposed method respect wellknown method stateoftheart Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mateusz Malinowski', 'Mario Fritz']

1610.01076v1

Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged. In this tutorial, we build a neural-based approach to answer questions about images. We base our tutorial on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the models that we present here can achieve a competitive performance on both datasets, in fact, they are among the best methods that use a combination of LSTM with a global, full frame CNN representation of an image. We hope that after reading this tutorial, the reader will be able to use Deep Learning frameworks, such as Keras and introduced Kraino, to build various architectures that will lead to a further performance improvement on this challenging task.

Tutorial on Answering Questions about Images with Deep Learning

http://arxiv.org/pdf/1610.01076v1

Title Tutorial Answering Questions Images Deep Learning Summary Together development accurate method Computer Vision Natural Language Understanding holistic architecture answer question content realworld image emerged tutorial build neuralbased approach answer question image base tutorial two datasets mostly DAQUAR bit VQA small tweak model present achieve competitive performance datasets fact among best method use combination LSTM global full frame CNN representation image hope reading tutorial reader able use Deep Learning framework Keras introduced Kraino build various architecture lead performance improvement challenging task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tony Beltramelli']

1705.07962v2

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).

pix2code: Generating Code from a Graphical User Interface Screenshot

http://arxiv.org/pdf/1705.07962v2

Title pix2code Generating Code Graphical User Interface Screenshot Summary Transforming graphical user interface screenshot created designer computer code typical task conducted developer order build customized software website mobile application paper show deep learning method leveraged train model endtoend automatically generate code single input image 77 accuracy three different platform ie iOS Android webbased technology Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Fred Richardson', 'Douglas Reynolds', 'Najim Dehak']

1504.00923v1

Learned feature representations and sub-phoneme posteriors from Deep Neural Networks (DNNs) have been used separately to produce significant performance gains for speaker and language recognition tasks. In this work we show how these gains are possible using a single DNN for both speaker and language recognition. The unified DNN approach is shown to yield substantial performance improvements on the the 2013 Domain Adaptation Challenge speaker recognition task (55% reduction in EER for the out-of-domain condition) and on the NIST 2011 Language Recognition Evaluation (48% reduction in EER for the 30s test condition).

A Unified Deep Neural Network for Speaker and Language Recognition

http://arxiv.org/pdf/1504.00923v1

Title Unified Deep Neural Network Speaker Language Recognition Summary Learned feature representation subphoneme posterior Deep Neural Networks DNNs used separately produce significant performance gain speaker language recognition task work show gain possible using single DNN speaker language recognition unified DNN approach shown yield substantial performance improvement 2013 Domain Adaptation Challenge speaker recognition task 55 reduction EER outofdomain condition NIST 2011 Language Recognition Evaluation 48 reduction EER 30 test condition Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hieu Pham', 'Melody Y. Guan', 'Barret Zoph', 'Quoc V. Le', 'Jeff Dean']

1802.03268v2

We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89%, which is on par with NASNet (Zoph et al., 2018), whose test error is 2.65%.

Efficient Neural Architecture Search via Parameter Sharing

http://arxiv.org/pdf/1802.03268v2

Title Efficient Neural Architecture Search via Parameter Sharing Summary propose Efficient Neural Architecture Search ENAS fast inexpensive approach automatic model design ENAS controller learns discover neural network architecture searching optimal subgraph within large computational graph controller trained policy gradient select subgraph maximizes expected reward validation set Meanwhile model corresponding selected subgraph trained minimize canonical cross entropy loss Thanks parameter sharing child model ENAS fast delivers strong empirical performance using much fewer GPUhours existing automatic model design approach notably 1000x le expensive standard Neural Architecture Search Penn Treebank dataset ENAS discovers novel architecture achieves test perplexity 558 establishing new stateoftheart among method without posttraining processing CIFAR10 dataset ENAS design novel architecture achieve test error 289 par NASNet Zoph et al 2018 whose test error 265 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Brenden M. Lake', 'Tomer D. Ullman', 'Joshua B. Tenenbaum', 'Samuel J. Gershman']

1604.00289v3

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

Building Machines That Learn and Think Like People

http://arxiv.org/pdf/1604.00289v3

Title Building Machines Learn Think Like People Summary Recent progress artificial intelligence AI renewed interest building system learn think like people Many advance come using deep neural network trained endtoend task object recognition video game board game achieving performance equal even beat human respect Despite biological inspiration performance achievement system differ human intelligence crucial way review progress cognitive science suggesting truly humanlike learning thinking machine reach beyond current engineering trend learn learn Specifically argue machine build causal model world support explanation understanding rather merely solving pattern recognition problem b ground learning intuitive theory physic psychology support enrich knowledge learned c harness compositionality learningtolearn rapidly acquire generalize knowledge new task situation suggest concrete challenge promising route towards goal combine strength recent neural network advance structured cognitive model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hao Wang', 'Dit-Yan Yeung']

1604.01662v2

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a general introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this survey, we also discuss the relationship and differences between Bayesian deep learning and other related topics like Bayesian treatment of neural networks.

Towards Bayesian Deep Learning: A Survey

http://arxiv.org/pdf/1604.01662v2

Title Towards Bayesian Deep Learning Survey Summary perception task visual object recognition text understanding play important role human intelligence subsequent task involve inference reasoning planning require even higher level intelligence past year seen major advance many perception task using deep learning model higherlevel inference however probabilistic graphical model Bayesian nature still powerful flexible achieve integrated intelligence involves perception inference naturally desirable tightly integrate deep learning Bayesian model within principled probabilistic framework call Bayesian deep learning unified framework perception text image using deep learning boost performance higherlevel inference return feedback inference process able enhance perception text image survey provides general introduction Bayesian deep learning review recent application recommender system topic model control survey also discus relationship difference Bayesian deep learning related topic like Bayesian treatment neural network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tejas D. Kulkarni', 'Karthik R. Narasimhan', 'Ardavan Saeedi', 'Joshua B. Tenenbaum']

1604.06057v2

Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for its own sake rather than to directly solve problems. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchical-DQN (h-DQN), a framework to integrate hierarchical value functions, operating at different temporal scales, with intrinsically motivated deep reinforcement learning. A top-level value function learns a policy over intrinsic goals, and a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications, such as functions over entities and relations. This provides an efficient space for exploration in complicated environments. We demonstrate the strength of our approach on two problems with very sparse, delayed feedback: (1) a complex discrete stochastic decision process, and (2) the classic ATARI game `Montezuma's Revenge'.

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

http://arxiv.org/pdf/1604.06057v2

Title Hierarchical Deep Reinforcement Learning Integrating Temporal Abstraction Intrinsic Motivation Summary Learning goaldirected behavior environment sparse feedback major challenge reinforcement learning algorithm primary difficulty arises due insufficient exploration resulting agent unable learn robust value function Intrinsically motivated agent explore new behavior sake rather directly solve problem intrinsic behavior could eventually help agent solve task posed environment present hierarchicalDQN hDQN framework integrate hierarchical value function operating different temporal scale intrinsically motivated deep reinforcement learning toplevel value function learns policy intrinsic goal lowerlevel function learns policy atomic action satisfy given goal hDQN allows flexible goal specification function entity relation provides efficient space exploration complicated environment demonstrate strength approach two problem sparse delayed feedback 1 complex discrete stochastic decision process 2 classic ATARI game Montezumas Revenge Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Deepak Pathak', 'Ross Girshick', 'Piotr Dollár', 'Trevor Darrell', 'Bharath Hariharan']

1612.06370v2

This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as 'pseudo ground truth' to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed 'pretext' tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce.

Learning Features by Watching Objects Move

http://arxiv.org/pdf/1612.06370v2

Title Learning Features Watching Objects Move Summary paper present novel yet intuitive approach unsupervised feature learning Inspired human visual system explore whether lowlevel motionbased grouping cue used learn effective visual representation Specifically use unsupervised motionbased segmentation video obtain segment use pseudo ground truth train convolutional network segment object single frame Given extensive evidence motion play key role development human visual system hope straightforward approach unsupervised learning effective cleverly designed pretext task studied literature Indeed extensive experiment show case used transfer learning object detection representation significantly outperforms previous unsupervised approach across multiple setting especially training data target task scarce Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Muhammad Ghifary', 'W. Bastiaan Kleijn', 'Mengjie Zhang']

1409.6041v1

We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set. We also show that our proposed model, preceded by the denoising auto-encoder pretraining, achieves better performance than recent benchmark models on the same data sets. This work represents the first study of MMD measure in the context of neural networks.

Domain Adaptive Neural Networks for Object Recognition

http://arxiv.org/pdf/1409.6041v1

Title Domain Adaptive Neural Networks Object Recognition Summary propose simple neural network model deal domain adaptation problem object recognition model incorporates Maximum Mean Discrepancy MMD measure regularization supervised learning reduce distribution mismatch source target domain latent space experiment demonstrate MMD regularization effective tool provide good domain adaptation model SURF feature raw image pixel particular image data set also show proposed model preceded denoising autoencoder pretraining achieves better performance recent benchmark model data set work represents first study MMD measure context neural network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lionel Pigou', 'Aäron van den Oord', 'Sander Dieleman', 'Mieke Van Herreweghe', 'Joni Dambre']

1506.01911v3

Recent studies have demonstrated the power of recurrent neural networks for machine translation, image captioning and speech recognition. For the task of capturing temporal structure in video, however, there still remain numerous open research questions. Current research suggests using a simple temporal feature pooling strategy to take into account the temporal aspect of video. We demonstrate that this method is not sufficient for gesture recognition, where temporal information is more discriminative compared to general video classification tasks. We explore deep architectures for gesture recognition in video and propose a new end-to-end trainable neural network architecture incorporating temporal convolutions and bidirectional recurrence. Our main contributions are twofold; first, we show that recurrence is crucial for this task; second, we show that adding temporal convolutions leads to significant improvements. We evaluate the different approaches on the Montalbano gesture recognition dataset, where we achieve state-of-the-art results.

Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video

http://arxiv.org/pdf/1506.01911v3

Title Beyond Temporal Pooling Recurrence Temporal Convolutions Gesture Recognition Video Summary Recent study demonstrated power recurrent neural network machine translation image captioning speech recognition task capturing temporal structure video however still remain numerous open research question Current research suggests using simple temporal feature pooling strategy take account temporal aspect video demonstrate method sufficient gesture recognition temporal information discriminative compared general video classification task explore deep architecture gesture recognition video propose new endtoend trainable neural network architecture incorporating temporal convolution bidirectional recurrence main contribution twofold first show recurrence crucial task second show adding temporal convolution lead significant improvement evaluate different approach Montalbano gesture recognition dataset achieve stateoftheart result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Rakesh Achanta', 'Trevor Hastie']

1509.05962v2

In this paper, we address the task of Optical Character Recognition(OCR) for the Telugu script. We present an end-to-end framework that segments the text image, classifies the characters and extracts lines using a language model. The segmentation is based on mathematical morphology. The classification module, which is the most challenging task of the three, is a deep convolutional neural network. The language is modelled as a third degree markov chain at the glyph level. Telugu script is a complex alphasyllabary and the language is agglutinative, making the problem hard. In this paper we apply the latest advances in neural networks to achieve state-of-the-art error rates. We also review convolutional neural networks in great detail and expound the statistical justification behind the many tricks needed to make Deep Learning work.

Telugu OCR Framework using Deep Learning

http://arxiv.org/pdf/1509.05962v2

Title Telugu OCR Framework using Deep Learning Summary paper address task Optical Character RecognitionOCR Telugu script present endtoend framework segment text image classifies character extract line using language model segmentation based mathematical morphology classification module challenging task three deep convolutional neural network language modelled third degree markov chain glyph level Telugu script complex alphasyllabary language agglutinative making problem hard paper apply latest advance neural network achieve stateoftheart error rate also review convolutional neural network great detail expound statistical justification behind many trick needed make Deep Learning work Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jeff Donahue', 'Philipp Krähenbühl', 'Trevor Darrell']

1605.09782v7

The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning.

Adversarial Feature Learning

http://arxiv.org/pdf/1605.09782v7

Title Adversarial Feature Learning Summary ability Generative Adversarial Networks GANs framework learn generative model mapping simple latent distribution arbitrarily complex data distribution demonstrated empirically compelling result showing latent space generator capture semantic variation data distribution Intuitively model trained predict semantic latent representation given data may serve useful feature representation auxiliary problem semantics relevant However existing form GANs mean learning inverse mapping projecting data back latent space propose Bidirectional Generative Adversarial Networks BiGANs mean learning inverse mapping demonstrate resulting learned feature representation useful auxiliary supervised discrimination task competitive contemporary approach unsupervised selfsupervised feature learning Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zachary C. Lipton']

1606.03490v3

Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and sometimes non-overlapping motivations for interpretability, and offer myriad notions of what attributes render models interpretable. Despite this ambiguity, many papers proclaim interpretability axiomatically, absent further explanation. In this paper, we seek to refine the discourse on interpretability. First, we examine the motivations underlying interest in interpretability, finding them to be diverse and occasionally discordant. Then, we address model properties and techniques thought to confer interpretability, identifying transparency to humans and post-hoc explanations as competing notions. Throughout, we discuss the feasibility and desirability of different notions, and question the oft-made assertions that linear models are interpretable and that deep neural networks are not.

The Mythos of Model Interpretability

http://arxiv.org/pdf/1606.03490v3

Title Mythos Model Interpretability Summary Supervised machine learning model boast remarkable predictive capability trust model work deployment else tell world want model good interpretable yet task interpretation appears underspecified Papers provide diverse sometimes nonoverlapping motivation interpretability offer myriad notion attribute render model interpretable Despite ambiguity many paper proclaim interpretability axiomatically absent explanation paper seek refine discourse interpretability First examine motivation underlying interest interpretability finding diverse occasionally discordant address model property technique thought confer interpretability identifying transparency human posthoc explanation competing notion Throughout discus feasibility desirability different notion question oftmade assertion linear model interpretable deep neural network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sahil Garg', 'Irina Rish', 'Guillermo Cecchi', 'Aurelie Lozano']

1701.06106v2

In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture. We propose a novel online dictionary-learning (sparse-coding) framework which incorporates the addition and deletion of hidden units (dictionary elements), and is inspired by the adult neurogenesis phenomenon in the dentate gyrus of the hippocampus, known to be associated with improved cognitive function and adaptation to new environments. In the online learning setting, where new input instances arrive sequentially in batches, the neuronal-birth is implemented by adding new units with random initial weights (random dictionary elements); the number of new units is determined by the current performance (representation error) of the dictionary, higher error causing an increase in the birth rate. Neuronal-death is implemented by imposing l1/l2-regularization (group sparsity) on the dictionary within the block-coordinate descent optimization at each iteration of our online alternating minimization scheme, which iterates between the code and dictionary updates. Finally, hidden unit connectivity adaptation is facilitated by introducing sparsity in dictionary elements. Our empirical evaluation on several real-life datasets (images and language) as well as on synthetic data demonstrates that the proposed approach can considerably outperform the state-of-art fixed-size (nonadaptive) online sparse coding of Mairal et al. (2009) in the presence of nonstationary data. Moreover, we identify certain properties of the data (e.g., sparse inputs with nearly non-overlapping supports) and of the model (e.g., dictionary sparsity) associated with such improvements.

Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World

http://arxiv.org/pdf/1701.06106v2

Title NeurogenesisInspired Dictionary Learning Online Model Adaption Changing World Summary paper focus online representation learning nonstationary environment may require continuous adaptation model architecture propose novel online dictionarylearning sparsecoding framework incorporates addition deletion hidden unit dictionary element inspired adult neurogenesis phenomenon dentate gyrus hippocampus known associated improved cognitive function adaptation new environment online learning setting new input instance arrive sequentially batch neuronalbirth implemented adding new unit random initial weight random dictionary element number new unit determined current performance representation error dictionary higher error causing increase birth rate Neuronaldeath implemented imposing l1l2regularization group sparsity dictionary within blockcoordinate descent optimization iteration online alternating minimization scheme iterates code dictionary update Finally hidden unit connectivity adaptation facilitated introducing sparsity dictionary element empirical evaluation several reallife datasets image language well synthetic data demonstrates proposed approach considerably outperform stateofart fixedsize nonadaptive online sparse coding Mairal et al 2009 presence nonstationary data Moreover identify certain property data eg sparse input nearly nonoverlapping support model eg dictionary sparsity associated improvement Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Weifeng Ge', 'Yizhou Yu']

1702.08690v2

Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In this paper, we introduce a source-target selective joint fine-tuning scheme for improving the performance of deep learning tasks with insufficient training data. In this scheme, a target learning task with insufficient training data is carried out simultaneously with another source learning task with abundant training data. However, the source learning task does not use all existing training data. Our core idea is to identify and use a subset of training images from the original source learning task whose low-level characteristics are similar to those from the target learning task, and jointly fine-tune shared convolutional layers for both tasks. Specifically, we compute descriptors from linear or nonlinear filter bank responses on training images from both tasks, and use such descriptors to search for a desired subset of training samples for the source learning task. Experiments demonstrate that our selective joint fine-tuning scheme achieves state-of-the-art performance on multiple visual classification tasks with insufficient training data for deep learning. Such tasks include Caltech 256, MIT Indoor 67, Oxford Flowers 102 and Stanford Dogs 120. In comparison to fine-tuning without a source domain, the proposed method can improve the classification accuracy by 2% - 10% using a single model.

Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning

http://arxiv.org/pdf/1702.08690v2

Title Borrowing Treasures Wealthy Deep Transfer Learning Selective Joint Finetuning Summary Deep neural network require large amount labeled training data supervised learning However collecting labeling much data might infeasible many case paper introduce sourcetarget selective joint finetuning scheme improving performance deep learning task insufficient training data scheme target learning task insufficient training data carried simultaneously another source learning task abundant training data However source learning task use existing training data core idea identify use subset training image original source learning task whose lowlevel characteristic similar target learning task jointly finetune shared convolutional layer task Specifically compute descriptor linear nonlinear filter bank response training image task use descriptor search desired subset training sample source learning task Experiments demonstrate selective joint finetuning scheme achieves stateoftheart performance multiple visual classification task insufficient training data deep learning task include Caltech 256 MIT Indoor 67 Oxford Flowers 102 Stanford Dogs 120 comparison finetuning without source domain proposed method improve classification accuracy 2 10 using single model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tanmay Gupta', 'Kevin Shih', 'Saurabh Singh', 'Derek Hoiem']

1704.00260v2

An important goal of computer vision is to build systems that learn visual representations over time that can be applied to many tasks. In this paper, we investigate a vision-language embedding as a core representation and show that it leads to better cross-task transfer than standard multi-task learning. In particular, the task of visual recognition is aligned to the task of visual question answering by forcing each to use the same word-region embeddings. We show this leads to greater inductive transfer from recognition to VQA than standard multitask learning. Visual recognition also improves, especially for categories that have relatively few recognition training labels but appear often in the VQA setting. Thus, our paper takes a small step towards creating more general vision systems by showing the benefit of interpretable, flexible, and trainable core representations.

Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks

http://arxiv.org/pdf/1704.00260v2

Title Aligned ImageWord Representations Improve Inductive Transfer Across VisionLanguage Tasks Summary important goal computer vision build system learn visual representation time applied many task paper investigate visionlanguage embedding core representation show lead better crosstask transfer standard multitask learning particular task visual recognition aligned task visual question answering forcing use wordregion embeddings show lead greater inductive transfer recognition VQA standard multitask learning Visual recognition also improves especially category relatively recognition training label appear often VQA setting Thus paper take small step towards creating general vision system showing benefit interpretable flexible trainable core representation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jan Hendrik Metzen', 'Mummadi Chaithanya Kumar', 'Thomas Brox', 'Volker Fischer']

1704.05712v3

While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise added to the input that was generated specifically to fool the system while being quasi-imperceptible for humans. More severely, there even exist universal perturbations that are input-agnostic but fool the network on the majority of inputs. While recent work has focused on image classification, this work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial perturbations that make the network yield a desired target segmentation as output. We show empirically that there exist barely perceptible universal noise patterns which result in nearly the same predicted segmentation for arbitrary inputs. Furthermore, we also show the existence of universal noise which removes a target class (e.g., all pedestrians) from the segmentation while leaving the segmentation mostly unchanged otherwise.

Universal Adversarial Perturbations Against Semantic Image Segmentation

http://arxiv.org/pdf/1704.05712v3

Title Universal Adversarial Perturbations Semantic Image Segmentation Summary deep learning remarkably successful perceptual task also shown vulnerable adversarial perturbation input perturbation denote noise added input generated specifically fool system quasiimperceptible human severely even exist universal perturbation inputagnostic fool network majority input recent work focused image classification work proposes attack semantic image segmentation present approach generating universal adversarial perturbation make network yield desired target segmentation output show empirically exist barely perceptible universal noise pattern result nearly predicted segmentation arbitrary input Furthermore also show existence universal noise remove target class eg pedestrian segmentation leaving segmentation mostly unchanged otherwise Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Quynh Nguyen', 'Matthias Hein']

1704.08045v2

While the optimization problem behind deep neural networks is highly non-convex, it is frequently observed in practice that training deep networks seems possible without getting stuck in suboptimal points. It has been argued that this is the case as all local minima are close to being globally optimal. We show that this is (almost) true, in fact almost all local minima are globally optimal, for a fully connected network with squared loss and analytic activation function given that the number of hidden units of one layer of the network is larger than the number of training points and the network structure from this layer on is pyramidal.

The loss surface of deep and wide neural networks

http://arxiv.org/pdf/1704.08045v2

Title loss surface deep wide neural network Summary optimization problem behind deep neural network highly nonconvex frequently observed practice training deep network seems possible without getting stuck suboptimal point argued case local minimum close globally optimal show almost true fact almost local minimum globally optimal fully connected network squared loss analytic activation function given number hidden unit one layer network larger number training point network structure layer pyramidal Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chris Donahue', 'Zachary C. Lipton', 'Akshay Balsubramani', 'Julian McAuley']

1705.07904v3

We propose a new algorithm for training generative adversarial networks that jointly learns latent codes for both identities (e.g. individual humans) and observations (e.g. specific photographs). By fixing the identity portion of the latent codes, we can generate diverse images of the same subject, and by fixing the observation portion, we can traverse the manifold of subjects while maintaining contingent aspects such as lighting and pose. Our algorithm features a pairwise training scheme in which each sample from the generator consists of two images with a common identity code. Corresponding samples from the real dataset consist of two distinct photographs of the same subject. In order to fool the discriminator, the generator must produce pairs that are photorealistic, distinct, and appear to depict the same individual. We augment both the DCGAN and BEGAN approaches with Siamese discriminators to facilitate pairwise training. Experiments with human judges and an off-the-shelf face verification system demonstrate our algorithm's ability to generate convincing, identity-matched photographs.

Semantically Decomposing the Latent Spaces of Generative Adversarial Networks

http://arxiv.org/pdf/1705.07904v3

Title Semantically Decomposing Latent Spaces Generative Adversarial Networks Summary propose new algorithm training generative adversarial network jointly learns latent code identity eg individual human observation eg specific photograph fixing identity portion latent code generate diverse image subject fixing observation portion traverse manifold subject maintaining contingent aspect lighting pose algorithm feature pairwise training scheme sample generator consists two image common identity code Corresponding sample real dataset consist two distinct photograph subject order fool discriminator generator must produce pair photorealistic distinct appear depict individual augment DCGAN BEGAN approach Siamese discriminator facilitate pairwise training Experiments human judge offtheshelf face verification system demonstrate algorithm ability generate convincing identitymatched photograph Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mahesh Chandra Mukkamala', 'Matthias Hein']

1706.05507v2

Adaptive gradient methods have become recently very popular, in particular as they have been shown to be useful in the training of deep neural networks. In this paper we have analyzed RMSProp, originally proposed for the training of deep neural networks, in the context of online convex optimization and show $\sqrt{T}$-type regret bounds. Moreover, we propose two variants SC-Adagrad and SC-RMSProp for which we show logarithmic regret bounds for strongly convex functions. Finally, we demonstrate in the experiments that these new variants outperform other adaptive gradient techniques or stochastic gradient descent in the optimization of strongly convex functions as well as in training of deep neural networks.

Variants of RMSProp and Adagrad with Logarithmic Regret Bounds

http://arxiv.org/pdf/1706.05507v2

Title Variants RMSProp Adagrad Logarithmic Regret Bounds Summary Adaptive gradient method become recently popular particular shown useful training deep neural network paper analyzed RMSProp originally proposed training deep neural network context online convex optimization show sqrtTtype regret bound Moreover propose two variant SCAdagrad SCRMSProp show logarithmic regret bound strongly convex function Finally demonstrate experiment new variant outperform adaptive gradient technique stochastic gradient descent optimization strongly convex function well training deep neural network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chunyuan Li', 'Hao Liu', 'Changyou Chen', 'Yunchen Pu', 'Liqun Chen', 'Ricardo Henao', 'Lawrence Carin']

1709.01215v2

We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as joint distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validated in synthetic data and real-world applications.

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

http://arxiv.org/pdf/1709.01215v2

Title ALICE Towards Understanding Adversarial Learning Joint Distribution Matching Summary investigate nonidentifiability issue associated bidirectional adversarial training joint distribution matching Within framework conditional entropy propose adversarial nonadversarial approach learn desirable matched joint distribution unsupervised supervised task unify broad family adversarial model joint distribution matching problem approach stabilizes learning unsupervised bidirectional adversarial learning method introduce extension semisupervised learning task Theoretical result validated synthetic data realworld application Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mateusz Buda', 'Atsuto Maki', 'Maciej A. Mazurowski']

1710.05381v1

In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i) the effect of class imbalance on classification performance is detrimental; (ii) the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii) oversampling should be applied to the level that totally eliminates the imbalance, whereas undersampling can perform better when the imbalance is only removed to some extent; (iv) as opposed to some classical machine learning models, oversampling does not necessarily cause overfitting of CNNs; (v) thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.

A systematic study of the class imbalance problem in convolutional neural networks

http://arxiv.org/pdf/1710.05381v1

Title systematic study class imbalance problem convolutional neural network Summary study systematically investigate impact class imbalance classification performance convolutional neural network CNNs compare frequently used method address issue Class imbalance common problem comprehensively studied classical machine learning yet limited systematic research available context deep learning study use three benchmark datasets increasing complexity MNIST CIFAR10 ImageNet investigate effect imbalance classification perform extensive comparison several method address issue oversampling undersampling twophase training thresholding compensates prior class probability main evaluation metric area receiver operating characteristic curve ROC AUC adjusted multiclass task since overall accuracy metric associated notable difficulty context imbalanced data Based result experiment conclude effect class imbalance classification performance detrimental ii method addressing class imbalance emerged dominant almost analyzed scenario oversampling iii oversampling applied level totally eliminates imbalance whereas undersampling perform better imbalance removed extent iv opposed classical machine learning model oversampling necessarily cause overfitting CNNs v thresholding applied compensate prior class probability overall number properly classified case interest Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jan Kukačka', 'Vladimir Golkov', 'Daniel Cremers']

1710.10686v1

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.

Regularization for Deep Learning: A Taxonomy

http://arxiv.org/pdf/1710.10686v1

Title Regularization Deep Learning Taxonomy Summary Regularization one crucial ingredient deep learning yet term regularization various definition regularization method often studied separately work present systematic unifying taxonomy categorize existing method distinguish method affect data network architecture error term regularization term optimization procedure provide detail listed method instead present overview method sorted meaningful category subcategories help revealing link fundamental similarity Finally include practical recommendation user developer new regularization method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Elie Aljalbout', 'Vladimir Golkov', 'Yawar Siddiqui', 'Daniel Cremers']

1801.07648v1

Clustering is a fundamental machine learning method. The quality of its results is dependent on the data distribution. For this reason, deep neural networks can be used for learning better representations of the data. In this paper, we propose a systematic taxonomy for clustering with deep learning, in addition to a review of methods from the field. Based on our taxonomy, creating new methods is more straightforward. We also propose a new approach which is built on the taxonomy and surpasses some of the limitations of some previous work. Our experimental evaluation on image datasets shows that the method approaches state-of-the-art clustering quality, and performs better in some cases.

Clustering with Deep Learning: Taxonomy and New Methods

http://arxiv.org/pdf/1801.07648v1

Title Clustering Deep Learning Taxonomy New Methods Summary Clustering fundamental machine learning method quality result dependent data distribution reason deep neural network used learning better representation data paper propose systematic taxonomy clustering deep learning addition review method field Based taxonomy creating new method straightforward also propose new approach built taxonomy surpasses limitation previous work experimental evaluation image datasets show method approach stateoftheart clustering quality performs better case Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Armand Zampieri', 'Guillaume Charpiat', 'Yuliya Tarabalka']

1802.09816v1

We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformation.We show their performance in terms of quality and speed through various tasks of remote sensing multimodal image alignment. In particular, we are able to register correctly cadastral maps of buildings as well as road polylines onto RGB images, and outperform current keypoint matching methods.

Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing

http://arxiv.org/pdf/1802.09816v1

Title Coarse fine nonrigid registration chain scalespecific neural network multimodal image alignment application remote sensing Summary tackle problem multimodal image nonrigid registration prime importance remote sensing medical imaging difficulty encountered classical registration approach include feature design slow optimization gradient descent analyzing method note significance notion scale design easytotrain fullyconvolutional neural network able learn scalespecific feature chained appropriately perform global registration linear time getting rid gradient descent scheme predicting directly deformationWe show performance term quality speed various task remote sensing multimodal image alignment particular able register correctly cadastral map building well road polylines onto RGB image outperform current keypoint matching method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Li Yao', 'Atousa Torabi', 'Kyunghyun Cho', 'Nicolas Ballas', 'Christopher Pal', 'Hugo Larochelle', 'Aaron Courville']

1502.08029v5

Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.

Describing Videos by Exploiting Temporal Structure

http://arxiv.org/pdf/1502.08029v5

Title Describing Videos Exploiting Temporal Structure Summary Recent progress using recurrent neural network RNNs image description motivated exploration application video description However image static working video requires modeling dynamic temporal structure properly integrating information natural language description context propose approach successfully take account local global temporal structure video produce description First approach incorporates spatial temporal 3D convolutional neural network 3D CNN representation short temporal dynamic 3D CNN representation trained video action recognition task produce representation tuned human motion behavior Second propose temporal attention mechanism allows go beyond local temporal modeling learns automatically select relevant temporal segment given textgenerating RNN approach exceeds current stateofart BLEU METEOR metric Youtube2Text dataset also present result new larger challenging dataset paired video natural language description Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hao Wang', 'Xingjian Shi', 'Dit-Yan Yeung']

1611.00454v1

Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.

Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

http://arxiv.org/pdf/1611.00454v1

Title Collaborative Recurrent Autoencoder Recommend Learning Fill Blanks Summary Hybrid method utilize content rating information commonly used many recommender system However use either handcrafted feature bagofwords representation surrogate content information neither effective natural enough address problem develop collaborative recurrent autoencoder CRAE denoising recurrent autoencoder DRAE model generation content sequence collaborative filtering CF setting model generalizes recent advance recurrent deep learning iid input noniid CFbased input provides new denoising scheme along novel learnable pooling scheme recurrent autoencoder first develop hierarchical Bayesian model DRAE generalize CF setting synergy denoising CF enables CRAE make accurate recommendation learning fill blank sequence Experiments realworld datasets different domain CiteULike Netflix show jointly modeling orderaware generation sequence content information performing CF rating CRAE able significantly outperform state art recommendation task based rating sequence generation task based content information Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Laura Graesser', 'Abhinav Gupta', 'Lakshay Sharma', 'Evelina Bakhturina']

1712.00725v1

In this project we analysed how much semantic information images carry, and how much value image data can add to sentiment analysis of the text associated with the images. To better understand the contribution from images, we compared models which only made use of image data, models which only made use of text data, and models which combined both data types. We also analysed if this approach could help sentiment classifiers generalize to unknown sentiments.

Sentiment Classification using Images and Label Embeddings

http://arxiv.org/pdf/1712.00725v1

Title Sentiment Classification using Images Label Embeddings Summary project analysed much semantic information image carry much value image data add sentiment analysis text associated image better understand contribution image compared model made use image data model made use text data model combined data type also analysed approach could help sentiment classifier generalize unknown sentiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hao Wang', 'Xingjian Shi', 'Dit-Yan Yeung']

1611.00448v1

Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN). Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models. To address these problems, we propose a class of probabilistic neural networks, dubbed natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential-family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation before producing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second-order representations for the associated tasks such as link prediction. Experiments on real-world datasets show that NPN can achieve state-of-the-art performance.

Natural-Parameter Networks: A Class of Probabilistic Neural Networks

http://arxiv.org/pdf/1611.00448v1

Title NaturalParameter Networks Class Probabilistic Neural Networks Summary Neural network NN achieved stateoftheart performance various application Unfortunately application training data insufficient often prone overfitting One effective way alleviate problem exploit Bayesian approach using Bayesian neural network BNN Another shortcoming NN lack flexibility customize different distribution weight neuron according data often done probabilistic graphical model address problem propose class probabilistic neural network dubbed naturalparameter network NPN novel lightweight Bayesian treatment NN NPN allows usage arbitrary exponentialfamily distribution model weight neuron Different traditional NN BNN NPN take distribution input go layer transformation producing distribution match target output distribution Bayesian treatment efficient backpropagation BP performed learn natural parameter distribution weight neuron output distribution layer byproduct may used secondorder representation associated task link prediction Experiments realworld datasets show NPN achieve stateoftheart performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Misha Denil', 'Pulkit Agrawal', 'Tejas D Kulkarni', 'Tom Erez', 'Peter Battaglia', 'Nando de Freitas']

1611.01843v3

When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way. This process of active interaction is in the same spirit as a scientist performing experiments to discover hidden facts. Recent advances in artificial intelligence have yielded machines that can achieve superhuman performance in Go, Atari, natural language processing, and complex control problems; however, it is not clear that these systems can rival the scientific intuition of even a young child. In this work we introduce a basic set of tasks that require agents to estimate properties such as mass and cohesion of objects in an interactive simulated environment where they can manipulate the objects and observe the consequences. We found that state of art deep reinforcement learning methods can learn to perform the experiments necessary to discover such hidden properties. By systematically manipulating the problem difficulty and the cost incurred by the agent for performing experiments, we found that agents learn different strategies that balance the cost of gathering information against the cost of making mistakes in different situations.

Learning to Perform Physics Experiments via Deep Reinforcement Learning

http://arxiv.org/pdf/1611.01843v3

Title Learning Perform Physics Experiments via Deep Reinforcement Learning Summary encountering novel object human able infer wide range physical property mass friction deformability interacting goal driven way process active interaction spirit scientist performing experiment discover hidden fact Recent advance artificial intelligence yielded machine achieve superhuman performance Go Atari natural language processing complex control problem however clear system rival scientific intuition even young child work introduce basic set task require agent estimate property mass cohesion object interactive simulated environment manipulate object observe consequence found state art deep reinforcement learning method learn perform experiment necessary discover hidden property systematically manipulating problem difficulty cost incurred agent performing experiment found agent learn different strategy balance cost gathering information cost making mistake different situation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tsung-Hsien Wen', 'David Vandyke', 'Nikola Mrksic', 'Milica Gasic', 'Lina M. Rojas-Barahona', 'Pei-Hao Su', 'Stefan Ultes', 'Steve Young']

1604.04562v3

Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each component. In this work we introduce a neural network-based text-in, text-out end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand. The results show that the model can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain.

A Network-based End-to-End Trainable Task-oriented Dialogue System

http://arxiv.org/pdf/1604.04562v3

Title Networkbased EndtoEnd Trainable Taskoriented Dialogue System Summary Teaching machine accomplish task conversing naturally human challenging Currently developing taskoriented dialogue system requires creating multiple component typically involves either large amount handcrafting acquiring costly labelled datasets solve statistical learning problem component work introduce neural networkbased textin textout endtoend trainable goaloriented dialogue system along new way collecting dialogue data based novel pipelined WizardofOz framework approach allows u develop dialogue system easily without making many assumption task hand result show model converse human subject naturally whilst helping accomplish task restaurant search domain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Johannes Welbl', 'Guillaume Bouchard', 'Sebastian Riedel']

1604.05878v1

Embedding-based Knowledge Base Completion models have so far mostly combined distributed representations of individual entities or relations to compute truth scores of missing links. Facts can however also be represented using pairwise embeddings, i.e. embeddings for pairs of entities and relations. In this paper we explore such bigram embeddings with a flexible Factorization Machine model and several ablations from it. We investigate the relevance of various bigram types on the fb15k237 dataset and find relative improvements compared to a compositional model.

A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion

http://arxiv.org/pdf/1604.05878v1

Title Factorization Machine Framework Testing Bigram Embeddings Knowledgebase Completion Summary Embeddingbased Knowledge Base Completion model far mostly combined distributed representation individual entity relation compute truth score missing link Facts however also represented using pairwise embeddings ie embeddings pair entity relation paper explore bigram embeddings flexible Factorization Machine model several ablation investigate relevance various bigram type fb15k237 dataset find relative improvement compared compositional model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Franck Dernoncourt', 'Ji Young Lee', 'Peter Szolovits']

1612.05251v1

Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction. Our model achieves state-of-the-art results on two different datasets for sequential sentence classification in medical abstracts.

Neural Networks for Joint Sentence Classification in Medical Paper Abstracts

http://arxiv.org/pdf/1612.05251v1

Title Neural Networks Joint Sentence Classification Medical Paper Abstracts Summary Existing model based artificial neural network ANNs sentence classification often incorporate context sentence appear classify sentence individually However traditional sentence classification approach shown greatly benefit jointly classifying subsequent sentence conditional random field work present ANN architecture combine effectiveness typical ANN model classify sentence isolation strength structured prediction model achieves stateoftheart result two different datasets sequential sentence classification medical abstract Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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-0.03922887146472931, -0.012865214608609676, 0.050107505172491074, 0.001542727230116725, -0.052450478076934814, -0.00371344480663538]

['Franck Dernoncourt', 'Ji Young Lee', 'Ozlem Uzuner', 'Peter Szolovits']

1606.03475v1

Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) defines 18 types of protected health information (PHI) that needs to be removed to de-identify patient notes. Manual de-identification is impractical given the size of EHR databases, the limited number of researchers with access to the non-de-identified notes, and the frequent mistakes of human annotators. A reliable automated de-identification system would consequently be of high value. Materials and Methods: We introduce the first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems. We compare the performance of the system with state-of-the-art systems on two datasets: the i2b2 2014 de-identification challenge dataset, which is the largest publicly available de-identification dataset, and the MIMIC de-identification dataset, which we assembled and is twice as large as the i2b2 2014 dataset. Results: Our ANN model outperforms the state-of-the-art systems. It yields an F1-score of 97.85 on the i2b2 2014 dataset, with a recall 97.38 and a precision of 97.32, and an F1-score of 99.23 on the MIMIC de-identification dataset, with a recall 99.25 and a precision of 99.06. Conclusion: Our findings support the use of ANNs for de-identification of patient notes, as they show better performance than previously published systems while requiring no feature engineering.

De-identification of Patient Notes with Recurrent Neural Networks

http://arxiv.org/pdf/1606.03475v1

Title Deidentification Patient Notes Recurrent Neural Networks Summary Objective Patient note electronic health record EHRs may contain critical information medical investigation However vast majority medical investigator access deidentified note order protect confidentiality patient United States Health Insurance Portability Accountability Act HIPAA defines 18 type protected health information PHI need removed deidentify patient note Manual deidentification impractical given size EHR database limited number researcher access nondeidentified note frequent mistake human annotator reliable automated deidentification system would consequently high value Materials Methods introduce first deidentification system based artificial neural network ANNs requires handcrafted feature rule unlike existing system compare performance system stateoftheart system two datasets i2b2 2014 deidentification challenge dataset largest publicly available deidentification dataset MIMIC deidentification dataset assembled twice large i2b2 2014 dataset Results ANN model outperforms stateoftheart system yield F1score 9785 i2b2 2014 dataset recall 9738 precision 9732 F1score 9923 MIMIC deidentification dataset recall 9925 precision 9906 Conclusion finding support use ANNs deidentification patient note show better performance previously published system requiring feature engineering Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tsendsuren Munkhdalai', 'Hong Yu']

1610.06454v2

Hypothesis testing is an important cognitive process that supports human reasoning. In this paper, we introduce a computational hypothesis testing approach based on memory augmented neural networks. Our approach involves a hypothesis testing loop that reconsiders and progressively refines a previously formed hypothesis in order to generate new hypotheses to test. We apply the proposed approach to language comprehension task by using Neural Semantic Encoders (NSE). Our NSE models achieve the state-of-the-art results showing an absolute improvement of 1.2% to 2.6% accuracy over previous results obtained by single and ensemble systems on standard machine comprehension benchmarks such as the Children's Book Test (CBT) and Who-Did-What (WDW) news article datasets.

Reasoning with Memory Augmented Neural Networks for Language Comprehension

http://arxiv.org/pdf/1610.06454v2

Title Reasoning Memory Augmented Neural Networks Language Comprehension Summary Hypothesis testing important cognitive process support human reasoning paper introduce computational hypothesis testing approach based memory augmented neural network approach involves hypothesis testing loop reconsiders progressively refines previously formed hypothesis order generate new hypothesis test apply proposed approach language comprehension task using Neural Semantic Encoders NSE NSE model achieve stateoftheart result showing absolute improvement 12 26 accuracy previous result obtained single ensemble system standard machine comprehension benchmark Childrens Book Test CBT WhoDidWhat WDW news article datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['W. James Murdoch', 'Arthur Szlam']

1702.02540v2

Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear. As a result, these models are generally treated as black boxes, yielding no insight of the underlying learned patterns. In this paper we consider Long Short Term Memory networks (LSTMs) and demonstrate a new approach for tracking the importance of a given input to the LSTM for a given output. By identifying consistently important patterns of words, we are able to distill state of the art LSTMs on sentiment analysis and question answering into a set of representative phrases. This representation is then quantitatively validated by using the extracted phrases to construct a simple, rule-based classifier which approximates the output of the LSTM.

Automatic Rule Extraction from Long Short Term Memory Networks

http://arxiv.org/pdf/1702.02540v2

Title Automatic Rule Extraction Long Short Term Memory Networks Summary Although deep learning model proven effective solving problem natural language processing mechanism come conclusion often unclear result model generally treated black box yielding insight underlying learned pattern paper consider Long Short Term Memory network LSTMs demonstrate new approach tracking importance given input LSTM given output identifying consistently important pattern word able distill state art LSTMs sentiment analysis question answering set representative phrase representation quantitatively validated using extracted phrase construct simple rulebased classifier approximates output LSTM Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sebastian Gehrmann', 'Franck Dernoncourt', 'Yeran Li', 'Eric T. Carlson', 'Joy T. Wu', 'Jonathan Welt', 'John Foote Jr.', 'Edward T. Moseley', 'David W. Grant', 'Patrick D. Tyler', 'Leo Anthony Celi']

1703.08705v1

Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a patient has a medical condition, and is a crucial part of secondary analysis of healthcare data. We assess the performance of deep learning algorithms and compare them with classical NLP approaches. Materials and Methods: We compare convolutional neural networks (CNNs), n-gram models, and approaches based on cTAKES that extract pre-defined medical concepts from clinical notes and use them to predict patient phenotypes. The performance is tested on 10 different phenotyping tasks using 1,610 discharge summaries extracted from the MIMIC-III database. Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our model having an F1-score up to 37 points higher than alternative approaches. We additionally assess the interpretability of our model by presenting a method that extracts the most salient phrases for a particular prediction. Conclusion: We show that NLP methods based on deep learning improve the performance of patient phenotyping. Our CNN-based algorithm automatically learns the phrases associated with each patient phenotype. As such, it reduces the annotation complexity for clinical domain experts, who are normally required to develop task-specific annotation rules and identify relevant phrases. Our method performs well in terms of both performance and interpretability, which indicates that deep learning is an effective approach to patient phenotyping based on clinicians' notes.

Comparing Rule-Based and Deep Learning Models for Patient Phenotyping

http://arxiv.org/pdf/1703.08705v1

Title Comparing RuleBased Deep Learning Models Patient Phenotyping Summary Objective investigate whether deep learning technique natural language processing NLP used efficiently patient phenotyping Patient phenotyping classification task determining whether patient medical condition crucial part secondary analysis healthcare data ass performance deep learning algorithm compare classical NLP approach Materials Methods compare convolutional neural network CNNs ngram model approach based cTAKES extract predefined medical concept clinical note use predict patient phenotype performance tested 10 different phenotyping task using 1610 discharge summary extracted MIMICIII database Results CNNs outperform phenotyping algorithm 10 task average F1score model 76 PPV 83 sensitivity 71 model F1score 37 point higher alternative approach additionally ass interpretability model presenting method extract salient phrase particular prediction Conclusion show NLP method based deep learning improve performance patient phenotyping CNNbased algorithm automatically learns phrase associated patient phenotype reduces annotation complexity clinical domain expert normally required develop taskspecific annotation rule identify relevant phrase method performs well term performance interpretability indicates deep learning effective approach patient phenotyping based clinician note Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ji Young Lee', 'Franck Dernoncourt', 'Peter Szolovits']

1704.01523v1

Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts, such as synonyms and hyponyms. Artificial neural networks have been recently explored for relation extraction. In this work, we continue this line of work and present a system based on a convolutional neural network to extract relations. Our model ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C).

MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks

http://arxiv.org/pdf/1704.01523v1

Title MIT SemEval2017 Task 10 Relation Extraction Convolutional Neural Networks Summary 50 million scholarly article published constitute unique repository knowledge particular one may infer relation scientific concept synonym hyponym Artificial neural network recently explored relation extraction work continue line work present system based convolutional neural network extract relation model ranked first SemEval2017 task 10 ScienceIE relation extraction scientific article subtask C Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ji Young Lee', 'Franck Dernoncourt', 'Peter Szolovits']

1705.06273v1

Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: label scarcity is particularly pronounced for patient note de-identification, which is an instance of NER. In this work, we analyze to what extent transfer learning may address this issue. In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.

Transfer Learning for Named-Entity Recognition with Neural Networks

http://arxiv.org/pdf/1705.06273v1

Title Transfer Learning NamedEntity Recognition Neural Networks Summary Recent approach based artificial neural network ANNs shown promising result namedentity recognition NER order achieve high performance ANNs need trained large labeled dataset However label might difficult obtain dataset user want perform NER label scarcity particularly pronounced patient note deidentification instance NER work analyze extent transfer learning may address issue particular demonstrate transferring ANN model trained large labeled dataset another dataset limited number label improves upon stateoftheart result two different datasets patient note deidentification Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sai Rajeswar', 'Sandeep Subramanian', 'Francis Dutil', 'Christopher Pal', 'Aaron Courville']

1705.10929v1

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.

Adversarial Generation of Natural Language

http://arxiv.org/pdf/1705.10929v1

Title Adversarial Generation Natural Language Summary Generative Adversarial Networks GANs gathered lot attention computer vision community yielding impressive result image generation Advances adversarial generation natural language noise however commensurate progress made generating image still lag far behind likelihood based method paper take step towards generating natural language GAN objective alone introduce simple baseline address discrete output space problem without relying gradient estimator show able achieve stateoftheart result Chinese poem generation dataset present quantitative result generating sentence contextfree probabilistic contextfree grammar qualitative language modeling result conditional version also described generate sequence conditioned sentence characteristic Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Leila Arras', 'Grégoire Montavon', 'Klaus-Robert Müller', 'Wojciech Samek']

1706.07206v2

Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.

Explaining Recurrent Neural Network Predictions in Sentiment Analysis

http://arxiv.org/pdf/1706.07206v2

Title Explaining Recurrent Neural Network Predictions Sentiment Analysis Summary Recently technique called Layerwise Relevance Propagation LRP shown deliver insightful explanation form input space relevance understanding feedforward neural network classification decision present work extend usage LRP recurrent neural network propose specific propagation rule applicable multiplicative connection arise recurrent network architecture LSTMs GRUs apply technique wordbased bidirectional LSTM model fiveclass sentiment prediction task evaluate resulting LRP relevance qualitatively quantitatively obtaining better result gradientbased related method used previous work Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Emmanuel Dufourq', 'Bruce A. Bassett']

1709.06990v1

Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression), which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification accuracy when used in conjunction with sentiment analysis algorithms. An analysis of PARSEC with eight commercial and non-commercial sentiment analysis algorithms on twelve English sentiment data sets reveals that accurate compression is possible with (0%, 1.3%, 3.3%) loss in sentiment classification accuracy for (20%, 50%, 75%) data compression with PARSEC using LingPipe, the most accurate of the sentiment algorithms. Other sentiment analysis algorithms are more severely affected by compression. We conclude that significant compression of text data is possible for sentiment analysis depending on the accuracy demands of the specific application and the specific sentiment analysis algorithm used.

Text Compression for Sentiment Analysis via Evolutionary Algorithms

http://arxiv.org/pdf/1709.06990v1

Title Text Compression Sentiment Analysis via Evolutionary Algorithms Summary textual data compressed intelligently without losing accuracy evaluating sentiment study propose novel evolutionary compression algorithm PARSEC PARtsofSpeech sEntiment Compression make use PartsofSpeech tag compress text way sacrifice minimal classification accuracy used conjunction sentiment analysis algorithm analysis PARSEC eight commercial noncommercial sentiment analysis algorithm twelve English sentiment data set reveals accurate compression possible 0 13 33 loss sentiment classification accuracy 20 50 75 data compression PARSEC using LingPipe accurate sentiment algorithm sentiment analysis algorithm severely affected compression conclude significant compression text data possible sentiment analysis depending accuracy demand specific application specific sentiment analysis algorithm used Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kartik Audhkhasi', 'Brian Kingsbury', 'Bhuvana Ramabhadran', 'George Saon', 'Michael Picheny']

1712.03133v1

Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received increasing attention compared to conventional sub-word based automatic speech recognition models using phones, characters, or context-dependent hidden Markov model states. This is because A2W models recognize words from speech without any decoder, pronunciation lexicon, or externally-trained language model, making training and decoding with such models simple. Prior work has shown that A2W models require orders of magnitude more training data in order to perform comparably to conventional models. Our work also showed this accuracy gap when using the English Switchboard-Fisher data set. This paper describes a recipe to train an A2W model that closes this gap and is at-par with state-of-the-art sub-word based models. We achieve a word error rate of 8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder or language model. We find that model initialization, training data order, and regularization have the most impact on the A2W model performance. Next, we present a joint word-character A2W model that learns to first spell the word and then recognize it. This model provides a rich output to the user instead of simple word hypotheses, making it especially useful in the case of words unseen or rarely-seen during training.

Building competitive direct acoustics-to-word models for English conversational speech recognition

http://arxiv.org/pdf/1712.03133v1

Title Building competitive direct acousticstoword model English conversational speech recognition Summary Direct acousticstoword A2W model endtoend paradigm received increasing attention compared conventional subword based automatic speech recognition model using phone character contextdependent hidden Markov model state A2W model recognize word speech without decoder pronunciation lexicon externallytrained language model making training decoding model simple Prior work shown A2W model require order magnitude training data order perform comparably conventional model work also showed accuracy gap using English SwitchboardFisher data set paper describes recipe train A2W model close gap atpar stateoftheart subword based model achieve word error rate 88139 Hub52000 SwitchboardCallHome test set without decoder language model find model initialization training data order regularization impact A2W model performance Next present joint wordcharacter A2W model learns first spell word recognize model provides rich output user instead simple word hypothesis making especially useful case word unseen rarelyseen training Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Huijuan Xu', 'Kate Saenko']

1511.05234v2

We address the problem of Visual Question Answering (VQA), which requires joint image and language understanding to answer a question about a given photograph. Recent approaches have applied deep image captioning methods based on convolutional-recurrent networks to this problem, but have failed to model spatial inference. To remedy this, we propose a model we call the Spatial Memory Network and apply it to the VQA task. Memory networks are recurrent neural networks with an explicit attention mechanism that selects certain parts of the information stored in memory. Our Spatial Memory Network stores neuron activations from different spatial regions of the image in its memory, and uses the question to choose relevant regions for computing the answer, a process of which constitutes a single "hop" in the network. We propose a novel spatial attention architecture that aligns words with image patches in the first hop, and obtain improved results by adding a second attention hop which considers the whole question to choose visual evidence based on the results of the first hop. To better understand the inference process learned by the network, we design synthetic questions that specifically require spatial inference and visualize the attention weights. We evaluate our model on two published visual question answering datasets, DAQUAR [1] and VQA [2], and obtain improved results compared to a strong deep baseline model (iBOWIMG) which concatenates image and question features to predict the answer [3].

Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering

http://arxiv.org/pdf/1511.05234v2

Title Ask Attend Answer Exploring QuestionGuided Spatial Attention Visual Question Answering Summary address problem Visual Question Answering VQA requires joint image language understanding answer question given photograph Recent approach applied deep image captioning method based convolutionalrecurrent network problem failed model spatial inference remedy propose model call Spatial Memory Network apply VQA task Memory network recurrent neural network explicit attention mechanism selects certain part information stored memory Spatial Memory Network store neuron activation different spatial region image memory us question choose relevant region computing answer process constitutes single hop network propose novel spatial attention architecture aligns word image patch first hop obtain improved result adding second attention hop considers whole question choose visual evidence based result first hop better understand inference process learned network design synthetic question specifically require spatial inference visualize attention weight evaluate model two published visual question answering datasets DAQUAR 1 VQA 2 obtain improved result compared strong deep baseline model iBOWIMG concatenates image question feature predict answer 3 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yuetan Lin', 'Zhangyang Pang', 'Donghui Wang', 'Yueting Zhuang']

1702.06700v1

Visual question answering (VQA) has witnessed great progress since May, 2015 as a classic problem unifying visual and textual data into a system. Many enlightening VQA works explore deep into the image and question encodings and fusing methods, of which attention is the most effective and infusive mechanism. Current attention based methods focus on adequate fusion of visual and textual features, but lack the attention to where people focus to ask questions about the image. Traditional attention based methods attach a single value to the feature at each spatial location, which losses many useful information. To remedy these problems, we propose a general method to perform saliency-like pre-selection on overlapped region features by the interrelation of bidirectional LSTM (BiLSTM), and use a novel element-wise multiplication based attention method to capture more competent correlation information between visual and textual features. We conduct experiments on the large-scale COCO-VQA dataset and analyze the effectiveness of our model demonstrated by strong empirical results.

Task-driven Visual Saliency and Attention-based Visual Question Answering

http://arxiv.org/pdf/1702.06700v1

Title Taskdriven Visual Saliency Attentionbased Visual Question Answering Summary Visual question answering VQA witnessed great progress since May 2015 classic problem unifying visual textual data system Many enlightening VQA work explore deep image question encoding fusing method attention effective infusive mechanism Current attention based method focus adequate fusion visual textual feature lack attention people focus ask question image Traditional attention based method attach single value feature spatial location loss many useful information remedy problem propose general method perform saliencylike preselection overlapped region feature interrelation bidirectional LSTM BiLSTM use novel elementwise multiplication based attention method capture competent correlation information visual textual feature conduct experiment largescale COCOVQA dataset analyze effectiveness model demonstrated strong empirical result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Akash Kumar Dhaka', 'Giampiero Salvi']

1606.09163v1

We present a systematic analysis on the performance of a phonetic recogniser when the window of input features is not symmetric with respect to the current frame. The recogniser is based on Context Dependent Deep Neural Networks (CD-DNNs) and Hidden Markov Models (HMMs). The objective is to reduce the latency of the system by reducing the number of future feature frames required to estimate the current output. Our tests performed on the TIMIT database show that the performance does not degrade when the input window is shifted up to 5 frames in the past compared to common practice (no future frame). This corresponds to improving the latency by 50 ms in our settings. Our tests also show that the best results are not obtained with the symmetric window commonly employed, but with an asymmetric window with eight past and two future context frames, although this observation should be confirmed on other data sets. The reduction in latency suggested by our results is critical for specific applications such as real-time lip synchronisation for tele-presence, but may also be beneficial in general applications to improve the lag in human-machine spoken interaction.

Optimising The Input Window Alignment in CD-DNN Based Phoneme Recognition for Low Latency Processing

http://arxiv.org/pdf/1606.09163v1

Title Optimising Input Window Alignment CDDNN Based Phoneme Recognition Low Latency Processing Summary present systematic analysis performance phonetic recogniser window input feature symmetric respect current frame recogniser based Context Dependent Deep Neural Networks CDDNNs Hidden Markov Models HMMs objective reduce latency system reducing number future feature frame required estimate current output test performed TIMIT database show performance degrade input window shifted 5 frame past compared common practice future frame corresponds improving latency 50 m setting test also show best result obtained symmetric window commonly employed asymmetric window eight past two future context frame although observation confirmed data set reduction latency suggested result critical specific application realtime lip synchronisation telepresence may also beneficial general application improve lag humanmachine spoken interaction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Peng Qian', 'Xipeng Qiu', 'Xuanjing Huang']

1604.06635v1

Recently, the long short-term memory neural network (LSTM) has attracted wide interest due to its success in many tasks. LSTM architecture consists of a memory cell and three gates, which looks similar to the neuronal networks in the brain. However, there still lacks the evidence of the cognitive plausibility of LSTM architecture as well as its working mechanism. In this paper, we study the cognitive plausibility of LSTM by aligning its internal architecture with the brain activity observed via fMRI when the subjects read a story. Experiment results show that the artificial memory vector in LSTM can accurately predict the observed sequential brain activities, indicating the correlation between LSTM architecture and the cognitive process of story reading.

Bridging LSTM Architecture and the Neural Dynamics during Reading

http://arxiv.org/pdf/1604.06635v1

Title Bridging LSTM Architecture Neural Dynamics Reading Summary Recently long shortterm memory neural network LSTM attracted wide interest due success many task LSTM architecture consists memory cell three gate look similar neuronal network brain However still lack evidence cognitive plausibility LSTM architecture well working mechanism paper study cognitive plausibility LSTM aligning internal architecture brain activity observed via fMRI subject read story Experiment result show artificial memory vector LSTM accurately predict observed sequential brain activity indicating correlation LSTM architecture cognitive process story reading Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jiwei Li']

1412.3714v2

This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition. We propose two models, Weighted Neural Network (WNN) and Binary-Expectation Neural Network (BENN), which automatically control how much one specific unit contributes to the higher-level representation. The proposed model can be viewed as incorporating a more powerful compositional function for embedding acquisition in recursive neural networks. Experimental results demonstrate the significant improvement over standard neural models.

Feature Weight Tuning for Recursive Neural Networks

http://arxiv.org/pdf/1412.3714v2

Title Feature Weight Tuning Recursive Neural Networks Summary paper address recursive neural network model automatically leave useless information emphasize important evidence word perform weight tuning higherlevel representation acquisition propose two model Weighted Neural Network WNN BinaryExpectation Neural Network BENN automatically control much one specific unit contributes higherlevel representation proposed model viewed incorporating powerful compositional function embedding acquisition recursive neural network Experimental result demonstrate significant improvement standard neural model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sadikin Mujiono', 'Mohamad Ivan Fanany', 'Chan Basaruddin']

1610.01891v1

One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text is special and has unique characteristics. In addition, the medical text mining poses more challenges, e.g., more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug. The mining is even more challenging due to the lack of labeled dataset sources and external knowledge, as well as multiple token representations for a single drug name that is more common in the real application setting. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, i.e., MLP (Multi-Layer Perceptrons). The second technique involves two deep network classifiers, i.e., DBN (Deep Belief Networks), and SAE (Stacked Denoising Encoders). The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, i.e., LSTM (Long Short Term Memory). In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645.

A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text

http://arxiv.org/pdf/1610.01891v1

Title New Data Representation Based Training Data Characteristics Extract Drug NamedEntity Medical Text Summary One essential task information extraction medical corpus drug name recognition Compared text source come domain medical text special unique characteristic addition medical text mining pose challenge eg unstructured text fast growing new term addition wide range name variation drug mining even challenging due lack labeled dataset source external knowledge well multiple token representation single drug name common real application setting Although many approach proposed overwhelm task problem remained poor Fscore performance le 075 paper present new treatment data representation technique overcome challenge propose three data representation technique based characteristic word distribution word similarity result word embedding training first technique evaluated standard NN model ie MLP MultiLayer Perceptrons second technique involves two deep network classifier ie DBN Deep Belief Networks SAE Stacked Denoising Encoders third technique represents sentence sequence evaluated recurrent NN model ie LSTM Long Short Term Memory extracting drug name entity third technique give best Fscore performance compared state art average Fscore 08645 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Eric Malmi', 'Pyry Takala', 'Hannu Toivonen', 'Tapani Raiko', 'Aristides Gionis']

1505.04771v2

Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the RankSVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.

DopeLearning: A Computational Approach to Rap Lyrics Generation

http://arxiv.org/pdf/1505.04771v2

Title DopeLearning Computational Approach Rap Lyrics Generation Summary Writing rap lyric requires creativity construct meaningful interesting story lyrical skill produce complex rhyme pattern form cornerstone good flow present rap lyric generation method capture aspect First develop prediction model identify next line existing lyric set candidate next line model based two machinelearning technique RankSVM algorithm deep neural network model novel structure Results show prediction model identify true next line among 299 randomly selected line accuracy 17 ie 50 time likely random Second employ prediction model combine line existing song producing lyric rhyme meaning evaluation produced lyric show term quantitative rhyme density method outperforms best human rapper 21 rap lyric generator deployed online tool called DeepBeat performance tool assessed analyzing usage log analysis show machinelearned ranking correlate user preference Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shengxian Wan', 'Yanyan Lan', 'Jun Xu', 'Jiafeng Guo', 'Liang Pang', 'Xueqi Cheng']

1604.04378v1

Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i.e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position. Based on this idea, we propose a novel deep architecture, namely Match-SRNN, to model the recursive matching structure. Firstly, a tensor is constructed to capture the word level interactions. Then a spatial RNN is applied to integrate the local interactions recursively, with importance determined by four types of gates. Finally, the matching score is calculated based on the global interaction. We show that, after degenerated to the exact matching scenario, Match-SRNN can approximate the dynamic programming process of longest common subsequence. Thus, there exists a clear interpretation for Match-SRNN. Our experiments on two semantic matching tasks showed the effectiveness of Match-SRNN, and its ability of visualizing the learned matching structure.

Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN

http://arxiv.org/pdf/1604.04378v1

Title MatchSRNN Modeling Recursive Matching Structure Spatial RNN Summary Semantic matching aim determine matching degree two text fundamental problem many NLP application Recently deep learning approach applied problem significant improvement achieved paper propose view generation global interaction two text recursive process ie interaction two text position composition interaction prefix well word level interaction current position Based idea propose novel deep architecture namely MatchSRNN model recursive matching structure Firstly tensor constructed capture word level interaction spatial RNN applied integrate local interaction recursively importance determined four type gate Finally matching score calculated based global interaction show degenerated exact matching scenario MatchSRNN approximate dynamic programming process longest common subsequence Thus exists clear interpretation MatchSRNN experiment two semantic matching task showed effectiveness MatchSRNN ability visualizing learned matching structure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian V. Serban', 'Alexander G. Ororbia II', 'Joelle Pineau', 'Aaron Courville']

1612.00377v4

Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.

Piecewise Latent Variables for Neural Variational Text Processing

http://arxiv.org/pdf/1612.00377v4

Title Piecewise Latent Variables Neural Variational Text Processing Summary Advances neural variational inference facilitated learning powerful directed graphical model continuous latent variable variational autoencoders hope model learn represent rich multimodal latent factor realworld data natural language text However current model often assume simplistic prior latent variable unimodal Gaussian distribution incapable representing complex latent factor efficiently overcome restriction propose simple highly flexible piecewise constant distribution distribution capacity represent exponential number mode latent target distribution remaining mathematically tractable result demonstrate incorporating new latent distribution different model yield substantial improvement natural language processing task document modeling natural language generation dialogue Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Baolin Peng', 'Kaisheng Yao']

1506.00195v1

Recurrent Neural Networks (RNNs) have become increasingly popular for the task of language understanding. In this task, a semantic tagger is deployed to associate a semantic label to each word in an input sequence. The success of RNN may be attributed to its ability to memorize long-term dependence that relates the current-time semantic label prediction to the observations many time instances away. However, the memory capacity of simple RNNs is limited because of the gradient vanishing and exploding problem. We propose to use an external memory to improve memorization capability of RNNs. We conducted experiments on the ATIS dataset, and observed that the proposed model was able to achieve the state-of-the-art results. We compare our proposed model with alternative models and report analysis results that may provide insights for future research.

Recurrent Neural Networks with External Memory for Language Understanding

http://arxiv.org/pdf/1506.00195v1

Title Recurrent Neural Networks External Memory Language Understanding Summary Recurrent Neural Networks RNNs become increasingly popular task language understanding task semantic tagger deployed associate semantic label word input sequence success RNN may attributed ability memorize longterm dependence relates currenttime semantic label prediction observation many time instance away However memory capacity simple RNNs limited gradient vanishing exploding problem propose use external memory improve memorization capability RNNs conducted experiment ATIS dataset observed proposed model able achieve stateoftheart result compare proposed model alternative model report analysis result may provide insight future research Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Alessandro Sordoni', 'Michel Galley', 'Michael Auli', 'Chris Brockett', 'Yangfeng Ji', 'Margaret Mitchell', 'Jian-Yun Nie', 'Jianfeng Gao', 'Bill Dolan']

1506.06714v1

We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

http://arxiv.org/pdf/1506.06714v1

Title Neural Network Approach ContextSensitive Generation Conversational Responses Summary present novel response generation system trained end end large quantity unstructured Twitter conversation neural network architecture used address sparsity issue arise integrating contextual information classic statistical model allowing system take account previous dialog utterance dynamiccontext generative model show consistent gain contextsensitive noncontextsensitive Machine Translation Information Retrieval baseline Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ryan Lowe', 'Nissan Pow', 'Iulian Serban', 'Joelle Pineau']

1506.08909v3

This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter. We also describe two neural learning architectures suitable for analyzing this dataset, and provide benchmark performance on the task of selecting the best next response.

The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

http://arxiv.org/pdf/1506.08909v3

Title Ubuntu Dialogue Corpus Large Dataset Research Unstructured MultiTurn Dialogue Systems Summary paper introduces Ubuntu Dialogue Corpus dataset containing almost 1 million multiturn dialogue total 7 million utterance 100 million word provides unique resource research building dialogue manager based neural language model make use large amount unlabeled data dataset multiturn property conversation Dialog State Tracking Challenge datasets unstructured nature interaction microblog service Twitter also describe two neural learning architecture suitable analyzing dataset provide benchmark performance task selecting best next response Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian V. Serban', 'Alessandro Sordoni', 'Yoshua Bengio', 'Aaron Courville', 'Joelle Pineau']

1507.04808v3

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

http://arxiv.org/pdf/1507.04808v3

Title Building EndToEnd Dialogue Systems Using Generative Hierarchical Neural Network Models Summary investigate task building open domain conversational dialogue system based large dialogue corpus using generative model Generative model produce system response autonomously generated wordbyword opening possibility realistic flexible interaction support goal extend recently proposed hierarchical recurrent encoderdecoder neural network dialogue domain demonstrate model competitive stateoftheart neural language model backoff ngram model investigate limitation similar approach show performance improved bootstrapping learning larger questionanswer pair corpus pretrained word embeddings Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dzmitry Bahdanau', 'Jan Chorowski', 'Dmitriy Serdyuk', 'Philemon Brakel', 'Yoshua Bengio']

1508.04395v2

Many of the current state-of-the-art Large Vocabulary Continuous Speech Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov Models (HMMs). Most of these systems contain separate components that deal with the acoustic modelling, language modelling and sequence decoding. We investigate a more direct approach in which the HMM is replaced with a Recurrent Neural Network (RNN) that performs sequence prediction directly at the character level. Alignment between the input features and the desired character sequence is learned automatically by an attention mechanism built into the RNN. For each predicted character, the attention mechanism scans the input sequence and chooses relevant frames. We propose two methods to speed up this operation: limiting the scan to a subset of most promising frames and pooling over time the information contained in neighboring frames, thereby reducing source sequence length. Integrating an n-gram language model into the decoding process yields recognition accuracies similar to other HMM-free RNN-based approaches.

End-to-End Attention-based Large Vocabulary Speech Recognition

http://arxiv.org/pdf/1508.04395v2

Title EndtoEnd Attentionbased Large Vocabulary Speech Recognition Summary Many current stateoftheart Large Vocabulary Continuous Speech Recognition Systems LVCSR hybrid neural network Hidden Markov Models HMMs system contain separate component deal acoustic modelling language modelling sequence decoding investigate direct approach HMM replaced Recurrent Neural Network RNN performs sequence prediction directly character level Alignment input feature desired character sequence learned automatically attention mechanism built RNN predicted character attention mechanism scan input sequence chooses relevant frame propose two method speed operation limiting scan subset promising frame pooling time information contained neighboring frame thereby reducing source sequence length Integrating ngram language model decoding process yield recognition accuracy similar HMMfree RNNbased approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Baolin Peng', 'Zhengdong Lu', 'Hang Li', 'Kam-Fai Wong']

1508.05508v1

We propose Neural Reasoner, a framework for neural network-based reasoning over natural language sentences. Given a question, Neural Reasoner can infer over multiple supporting facts and find an answer to the question in specific forms. Neural Reasoner has 1) a specific interaction-pooling mechanism, allowing it to examine multiple facts, and 2) a deep architecture, allowing it to model the complicated logical relations in reasoning tasks. Assuming no particular structure exists in the question and facts, Neural Reasoner is able to accommodate different types of reasoning and different forms of language expressions. Despite the model complexity, Neural Reasoner can still be trained effectively in an end-to-end manner. Our empirical studies show that Neural Reasoner can outperform existing neural reasoning systems with remarkable margins on two difficult artificial tasks (Positional Reasoning and Path Finding) proposed in [8]. For example, it improves the accuracy on Path Finding(10K) from 33.4% [6] to over 98%.

Towards Neural Network-based Reasoning

http://arxiv.org/pdf/1508.05508v1

Title Towards Neural Networkbased Reasoning Summary propose Neural Reasoner framework neural networkbased reasoning natural language sentence Given question Neural Reasoner infer multiple supporting fact find answer question specific form Neural Reasoner 1 specific interactionpooling mechanism allowing examine multiple fact 2 deep architecture allowing model complicated logical relation reasoning task Assuming particular structure exists question fact Neural Reasoner able accommodate different type reasoning different form language expression Despite model complexity Neural Reasoner still trained effectively endtoend manner empirical study show Neural Reasoner outperform existing neural reasoning system remarkable margin two difficult artificial task Positional Reasoning Path Finding proposed 8 example improves accuracy Path Finding10K 334 6 98 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hongyuan Mei', 'Mohit Bansal', 'Matthew R. Walter']

1509.00838v2

We propose an end-to-end, domain-independent neural encoder-aligner-decoder model for selective generation, i.e., the joint task of content selection and surface realization. Our model first encodes a full set of over-determined database event records via an LSTM-based recurrent neural network, then utilizes a novel coarse-to-fine aligner to identify the small subset of salient records to talk about, and finally employs a decoder to generate free-form descriptions of the aligned, selected records. Our model achieves the best selection and generation results reported to-date (with 59% relative improvement in generation) on the benchmark WeatherGov dataset, despite using no specialized features or linguistic resources. Using an improved k-nearest neighbor beam filter helps further. We also perform a series of ablations and visualizations to elucidate the contributions of our key model components. Lastly, we evaluate the generalizability of our model on the RoboCup dataset, and get results that are competitive with or better than the state-of-the-art, despite being severely data-starved.

What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment

http://arxiv.org/pdf/1509.00838v2

Title talk Selective Generation using LSTMs CoarsetoFine Alignment Summary propose endtoend domainindependent neural encoderalignerdecoder model selective generation ie joint task content selection surface realization model first encodes full set overdetermined database event record via LSTMbased recurrent neural network utilizes novel coarsetofine aligner identify small subset salient record talk finally employ decoder generate freeform description aligned selected record model achieves best selection generation result reported todate 59 relative improvement generation benchmark WeatherGov dataset despite using specialized feature linguistic resource Using improved knearest neighbor beam filter help also perform series ablation visualization elucidate contribution key model component Lastly evaluate generalizability model RoboCup dataset get result competitive better stateoftheart despite severely datastarved Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tim Rocktäschel', 'Edward Grefenstette', 'Karl Moritz Hermann', 'Tomáš Kočiský', 'Phil Blunsom']

1509.06664v4

While most approaches to automatically recognizing entailment relations have used classifiers employing hand engineered features derived from complex natural language processing pipelines, in practice their performance has been only slightly better than bag-of-word pair classifiers using only lexical similarity. The only attempt so far to build an end-to-end differentiable neural network for entailment failed to outperform such a simple similarity classifier. In this paper, we propose a neural model that reads two sentences to determine entailment using long short-term memory units. We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases. Furthermore, we present a qualitative analysis of attention weights produced by this model, demonstrating such reasoning capabilities. On a large entailment dataset this model outperforms the previous best neural model and a classifier with engineered features by a substantial margin. It is the first generic end-to-end differentiable system that achieves state-of-the-art accuracy on a textual entailment dataset.

Reasoning about Entailment with Neural Attention

http://arxiv.org/pdf/1509.06664v4

Title Reasoning Entailment Neural Attention Summary approach automatically recognizing entailment relation used classifier employing hand engineered feature derived complex natural language processing pipeline practice performance slightly better bagofword pair classifier using lexical similarity attempt far build endtoend differentiable neural network entailment failed outperform simple similarity classifier paper propose neural model read two sentence determine entailment using long shortterm memory unit extend model wordbyword neural attention mechanism encourages reasoning entailment pair word phrase Furthermore present qualitative analysis attention weight produced model demonstrating reasoning capability large entailment dataset model outperforms previous best neural model classifier engineered feature substantial margin first generic endtoend differentiable system achieves stateoftheart accuracy textual entailment dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yu Zhang', 'Guoguo Chen', 'Dong Yu', 'Kaisheng Yao', 'Sanjeev Khudanpur', 'James Glass']

1510.08983v2

In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains $43.9/47.7\%$ WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with $15.7\%$ and $5.3\%$ relative improvement respectively.

Highway Long Short-Term Memory RNNs for Distant Speech Recognition

http://arxiv.org/pdf/1510.08983v2

Title Highway Long ShortTerm Memory RNNs Distant Speech Recognition Summary paper extend deep long shortterm memory DLSTM recurrent neural network introducing gated direct connection memory cell adjacent layer direct link called highway connection enable unimpeded information flow across different layer thus alleviate gradient vanishing problem building deeper LSTMs introduce latencycontrolled bidirectional LSTMs BLSTMs exploit whole history keeping latency control Efficient algorithm proposed train novel network using frame sequence discriminative criterion Experiments AMI distant speech recognition DSR task indicate train deeper LSTMs achieve better improvement sequence training highway LSTMs HLSTMs novel model obtains 439477 WER AMI SDM dev eval set outperforming previous work beat strong DNN DLSTM baseline 157 53 relative improvement respectively Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pengcheng Yin', 'Zhengdong Lu', 'Hang Li', 'Ben Kao']

1512.00965v2

We proposed Neural Enquirer as a neural network architecture to execute a natural language (NL) query on a knowledge-base (KB) for answers. Basically, Neural Enquirer finds the distributed representation of a query and then executes it on knowledge-base tables to obtain the answer as one of the values in the tables. Unlike similar efforts in end-to-end training of semantic parsers, Neural Enquirer is fully "neuralized": it not only gives distributional representation of the query and the knowledge-base, but also realizes the execution of compositional queries as a series of differentiable operations, with intermediate results (consisting of annotations of the tables at different levels) saved on multiple layers of memory. Neural Enquirer can be trained with gradient descent, with which not only the parameters of the controlling components and semantic parsing component, but also the embeddings of the tables and query words can be learned from scratch. The training can be done in an end-to-end fashion, but it can take stronger guidance, e.g., the step-by-step supervision for complicated queries, and benefit from it. Neural Enquirer is one step towards building neural network systems which seek to understand language by executing it on real-world. Our experiments show that Neural Enquirer can learn to execute fairly complicated NL queries on tables with rich structures.

Neural Enquirer: Learning to Query Tables with Natural Language

http://arxiv.org/pdf/1512.00965v2

Title Neural Enquirer Learning Query Tables Natural Language Summary proposed Neural Enquirer neural network architecture execute natural language NL query knowledgebase KB answer Basically Neural Enquirer find distributed representation query executes knowledgebase table obtain answer one value table Unlike similar effort endtoend training semantic parser Neural Enquirer fully neuralized give distributional representation query knowledgebase also realizes execution compositional query series differentiable operation intermediate result consisting annotation table different level saved multiple layer memory Neural Enquirer trained gradient descent parameter controlling component semantic parsing component also embeddings table query word learned scratch training done endtoend fashion take stronger guidance eg stepbystep supervision complicated query benefit Neural Enquirer one step towards building neural network system seek understand language executing realworld experiment show Neural Enquirer learn execute fairly complicated NL query table rich structure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Petr Baudiš', 'Jan Pichl', 'Tomáš Vyskočil', 'Jan Šedivý']

1603.06127v4

We review the task of Sentence Pair Scoring, popular in the literature in various forms - viewed as Answer Sentence Selection, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment, Paraphrasing or e.g. a component of Memory Networks. We argue that all such tasks are similar from the model perspective and propose new baselines by comparing the performance of common IR metrics and popular convolutional, recurrent and attention-based neural models across many Sentence Pair Scoring tasks and datasets. We discuss the problem of evaluating randomized models, propose a statistically grounded methodology, and attempt to improve comparisons by releasing new datasets that are much harder than some of the currently used well explored benchmarks. We introduce a unified open source software framework with easily pluggable models and tasks, which enables us to experiment with multi-task reusability of trained sentence model. We set a new state-of-art in performance on the Ubuntu Dialogue dataset.

Sentence Pair Scoring: Towards Unified Framework for Text Comprehension

http://arxiv.org/pdf/1603.06127v4

Title Sentence Pair Scoring Towards Unified Framework Text Comprehension Summary review task Sentence Pair Scoring popular literature various form viewed Answer Sentence Selection Semantic Text Scoring Next Utterance Ranking Recognizing Textual Entailment Paraphrasing eg component Memory Networks argue task similar model perspective propose new baseline comparing performance common IR metric popular convolutional recurrent attentionbased neural model across many Sentence Pair Scoring task datasets discus problem evaluating randomized model propose statistically grounded methodology attempt improve comparison releasing new datasets much harder currently used well explored benchmark introduce unified open source software framework easily pluggable model task enables u experiment multitask reusability trained sentence model set new stateofart performance Ubuntu Dialogue dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jiatao Gu', 'Zhengdong Lu', 'Hang Li', 'Victor O. K. Li']

1603.06393v3

We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which certain segments in the input sequence are selectively replicated in the output sequence. A similar phenomenon is observable in human language communication. For example, humans tend to repeat entity names or even long phrases in conversation. The challenge with regard to copying in Seq2Seq is that new machinery is needed to decide when to perform the operation. In this paper, we incorporate copying into neural network-based Seq2Seq learning and propose a new model called CopyNet with encoder-decoder structure. CopyNet can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence. Our empirical study on both synthetic data sets and real world data sets demonstrates the efficacy of CopyNet. For example, CopyNet can outperform regular RNN-based model with remarkable margins on text summarization tasks.

Incorporating Copying Mechanism in Sequence-to-Sequence Learning

http://arxiv.org/pdf/1603.06393v3

Title Incorporating Copying Mechanism SequencetoSequence Learning Summary address important problem sequencetosequence Seq2Seq learning referred copying certain segment input sequence selectively replicated output sequence similar phenomenon observable human language communication example human tend repeat entity name even long phrase conversation challenge regard copying Seq2Seq new machinery needed decide perform operation paper incorporate copying neural networkbased Seq2Seq learning propose new model called CopyNet encoderdecoder structure CopyNet nicely integrate regular way word generation decoder new copying mechanism choose subsequence input sequence put proper place output sequence empirical study synthetic data set real world data set demonstrates efficacy CopyNet example CopyNet outperform regular RNNbased model remarkable margin text summarization task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian Vlad Serban', 'Alberto García-Durán', 'Caglar Gulcehre', 'Sungjin Ahn', 'Sarath Chandar', 'Aaron Courville', 'Yoshua Bengio']

1603.06807v2

Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.

Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

http://arxiv.org/pdf/1603.06807v2

Title Generating Factoid Questions Recurrent Neural Networks 30M Factoid QuestionAnswer Corpus Summary past decade largescale supervised learning corpus enabled machine learning researcher make substantial advance However date largescale questionanswer corpus available paper present 30M Factoid QuestionAnswer Corpus enormous question answer pair corpus produced applying novel neural network architecture knowledge base Freebase transduce fact natural language question produced question answer pair evaluated human evaluator using automatic evaluation metric including wellestablished machine translation sentence similarity metric Across evaluation criterion questiongeneration model outperforms competing templatebased baseline Furthermore presented human evaluator generated question appear comparable quality real humangenerated question Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chia-Wei Liu', 'Ryan Lowe', 'Iulian V. Serban', 'Michael Noseworthy', 'Laurent Charlin', 'Joelle Pineau']

1603.08023v2

We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available. Recent works in response generation have adopted metrics from machine translation to compare a model's generated response to a single target response. We show that these metrics correlate very weakly with human judgements in the non-technical Twitter domain, and not at all in the technical Ubuntu domain. We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future development of better automatic evaluation metrics for dialogue systems.

How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

http://arxiv.org/pdf/1603.08023v2

Title Evaluate Dialogue System Empirical Study Unsupervised Evaluation Metrics Dialogue Response Generation Summary investigate evaluation metric dialogue response generation system supervised label task completion available Recent work response generation adopted metric machine translation compare model generated response single target response show metric correlate weakly human judgement nontechnical Twitter domain technical Ubuntu domain provide quantitative qualitative result highlighting specific weakness existing metric provide recommendation future development better automatic evaluation metric dialogue system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian Vlad Serban', 'Alessandro Sordoni', 'Ryan Lowe', 'Laurent Charlin', 'Joelle Pineau', 'Aaron Courville', 'Yoshua Bengio']

1605.06069v3

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue. In an effort to model this kind of generative process, we propose a neural network-based generative architecture, with latent stochastic variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with recent neural network architectures. We evaluate the model performance through automatic evaluation metrics and by carrying out a human evaluation. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate the generation of long outputs and maintain the context.

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

http://arxiv.org/pdf/1605.06069v3

Title Hierarchical Latent Variable EncoderDecoder Model Generating Dialogues Summary Sequential data often posse hierarchical structure complex dependency subsequence found utterance dialogue effort model kind generative process propose neural networkbased generative architecture latent stochastic variable span variable number time step apply proposed model task dialogue response generation compare recent neural network architecture evaluate model performance automatic evaluation metric carrying human evaluation experiment demonstrate model improves upon recently proposed model latent variable facilitate generation long output maintain context Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dirk Weissenborn']

1606.03864v2

Many important NLP problems can be posed as dual-sequence or sequence-to-sequence modeling tasks. Recent advances in building end-to-end neural architectures have been highly successful in solving such tasks. In this work we propose a new architecture for dual-sequence modeling that is based on associative memory. We derive AM-RNNs, a recurrent associative memory (AM) which augments generic recurrent neural networks (RNN). This architecture is extended to the Dual AM-RNN which operates on two AMs at once. Our models achieve very competitive results on textual entailment. A qualitative analysis demonstrates that long range dependencies between source and target-sequence can be bridged effectively using Dual AM-RNNs. However, an initial experiment on auto-encoding reveals that these benefits are not exploited by the system when learning to solve sequence-to-sequence tasks which indicates that additional supervision or regularization is needed.

Neural Associative Memory for Dual-Sequence Modeling

http://arxiv.org/pdf/1606.03864v2

Title Neural Associative Memory DualSequence Modeling Summary Many important NLP problem posed dualsequence sequencetosequence modeling task Recent advance building endtoend neural architecture highly successful solving task work propose new architecture dualsequence modeling based associative memory derive AMRNNs recurrent associative memory augments generic recurrent neural network RNN architecture extended Dual AMRNN operates two AMs model achieve competitive result textual entailment qualitative analysis demonstrates long range dependency source targetsequence bridged effectively using Dual AMRNNs However initial experiment autoencoding reveals benefit exploited system learning solve sequencetosequence task indicates additional supervision regularization needed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Marc Dymetman', 'Chunyang Xiao']

1607.02467v2

We introduce LL-RNNs (Log-Linear RNNs), an extension of Recurrent Neural Networks that replaces the softmax output layer by a log-linear output layer, of which the softmax is a special case. This conceptually simple move has two main advantages. First, it allows the learner to combat training data sparsity by allowing it to model words (or more generally, output symbols) as complex combinations of attributes without requiring that each combination is directly observed in the training data (as the softmax does). Second, it permits the inclusion of flexible prior knowledge in the form of a priori specified modular features, where the neural network component learns to dynamically control the weights of a log-linear distribution exploiting these features. We conduct experiments in the domain of language modelling of French, that exploit morphological prior knowledge and show an important decrease in perplexity relative to a baseline RNN. We provide other motivating iillustrations, and finally argue that the log-linear and the neural-network components contribute complementary strengths to the LL-RNN: the LL aspect allows the model to incorporate rich prior knowledge, while the NN aspect, according to the "representation learning" paradigm, allows the model to discover novel combination of characteristics.

Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge

http://arxiv.org/pdf/1607.02467v2

Title LogLinear RNNs Towards Recurrent Neural Networks Flexible Prior Knowledge Summary introduce LLRNNs LogLinear RNNs extension Recurrent Neural Networks replaces softmax output layer loglinear output layer softmax special case conceptually simple move two main advantage First allows learner combat training data sparsity allowing model word generally output symbol complex combination attribute without requiring combination directly observed training data softmax Second permit inclusion flexible prior knowledge form priori specified modular feature neural network component learns dynamically control weight loglinear distribution exploiting feature conduct experiment domain language modelling French exploit morphological prior knowledge show important decrease perplexity relative baseline RNN provide motivating iillustrations finally argue loglinear neuralnetwork component contribute complementary strength LLRNN aspect allows model incorporate rich prior knowledge NN aspect according representation learning paradigm allows model discover novel combination characteristic Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ondrej Bajgar', 'Rudolf Kadlec', 'Jan Kleindienst']

1610.00956v1

There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article is making a case for the community to move to larger data and as a step in that direction it is proposing the BookTest, a new dataset similar to the popular Children's Book Test (CBT), however more than 60 times larger. We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture. On one version of the dataset our ensemble even exceeds the human baseline provided by Facebook. We then show in our own human study that there is still space for further improvement.

Embracing data abundance: BookTest Dataset for Reading Comprehension

http://arxiv.org/pdf/1610.00956v1

Title Embracing data abundance BookTest Dataset Reading Comprehension Summary practically unlimited amount natural language data available Still recent work text comprehension focused datasets small relative current computing possibility article making case community move larger data step direction proposing BookTest new dataset similar popular Childrens Book Test CBT however 60 time larger show training new data improves accuracy AttentionSum Reader model original CBT test data much larger margin many recent attempt improve model architecture one version dataset ensemble even exceeds human baseline provided Facebook show human study still space improvement Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['James Bradbury', 'Stephen Merity', 'Caiming Xiong', 'Richard Socher']

1611.01576v2

Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep's computation on the previous timestep's output limits parallelism and makes RNNs unwieldy for very long sequences. We introduce quasi-recurrent neural networks (QRNNs), an approach to neural sequence modeling that alternates convolutional layers, which apply in parallel across timesteps, and a minimalist recurrent pooling function that applies in parallel across channels. Despite lacking trainable recurrent layers, stacked QRNNs have better predictive accuracy than stacked LSTMs of the same hidden size. Due to their increased parallelism, they are up to 16 times faster at train and test time. Experiments on language modeling, sentiment classification, and character-level neural machine translation demonstrate these advantages and underline the viability of QRNNs as a basic building block for a variety of sequence tasks.

Quasi-Recurrent Neural Networks

http://arxiv.org/pdf/1611.01576v2

Title QuasiRecurrent Neural Networks Summary Recurrent neural network powerful tool modeling sequential data dependence timesteps computation previous timesteps output limit parallelism make RNNs unwieldy long sequence introduce quasirecurrent neural network QRNNs approach neural sequence modeling alternate convolutional layer apply parallel across timesteps minimalist recurrent pooling function applies parallel across channel Despite lacking trainable recurrent layer stacked QRNNs better predictive accuracy stacked LSTMs hidden size Due increased parallelism 16 time faster train test time Experiments language modeling sentiment classification characterlevel neural machine translation demonstrate advantage underline viability QRNNs basic building block variety sequence task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jakob N. Foerster', 'Justin Gilmer', 'Jan Chorowski', 'Jascha Sohl-Dickstein', 'David Sussillo']

1611.09434v2

There exist many problem domains where the interpretability of neural network models is essential for deployment. Here we introduce a recurrent architecture composed of input-switched affine transformations - in other words an RNN without any explicit nonlinearities, but with input-dependent recurrent weights. This simple form allows the RNN to be analyzed via straightforward linear methods: we can exactly characterize the linear contribution of each input to the model predictions; we can use a change-of-basis to disentangle input, output, and computational hidden unit subspaces; we can fully reverse-engineer the architecture's solution to a simple task. Despite this ease of interpretation, the input switched affine network achieves reasonable performance on a text modeling tasks, and allows greater computational efficiency than networks with standard nonlinearities.

Input Switched Affine Networks: An RNN Architecture Designed for Interpretability

http://arxiv.org/pdf/1611.09434v2

Title Input Switched Affine Networks RNN Architecture Designed Interpretability Summary exist many problem domain interpretability neural network model essential deployment introduce recurrent architecture composed inputswitched affine transformation word RNN without explicit nonlinearities inputdependent recurrent weight simple form allows RNN analyzed via straightforward linear method exactly characterize linear contribution input model prediction use changeofbasis disentangle input output computational hidden unit subspace fully reverseengineer architecture solution simple task Despite ease interpretation input switched affine network achieves reasonable performance text modeling task allows greater computational efficiency network standard nonlinearities Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Michał Daniluk', 'Tim Rocktäschel', 'Johannes Welbl', 'Sebastian Riedel']

1702.04521v1

Neural language models predict the next token using a latent representation of the immediate token history. Recently, various methods for augmenting neural language models with an attention mechanism over a differentiable memory have been proposed. For predicting the next token, these models query information from a memory of the recent history which can facilitate learning mid- and long-range dependencies. However, conventional attention mechanisms used in memory-augmented neural language models produce a single output vector per time step. This vector is used both for predicting the next token as well as for the key and value of a differentiable memory of a token history. In this paper, we propose a neural language model with a key-value attention mechanism that outputs separate representations for the key and value of a differentiable memory, as well as for encoding the next-word distribution. This model outperforms existing memory-augmented neural language models on two corpora. Yet, we found that our method mainly utilizes a memory of the five most recent output representations. This led to the unexpected main finding that a much simpler model based only on the concatenation of recent output representations from previous time steps is on par with more sophisticated memory-augmented neural language models.

Frustratingly Short Attention Spans in Neural Language Modeling

http://arxiv.org/pdf/1702.04521v1

Title Frustratingly Short Attention Spans Neural Language Modeling Summary Neural language model predict next token using latent representation immediate token history Recently various method augmenting neural language model attention mechanism differentiable memory proposed predicting next token model query information memory recent history facilitate learning mid longrange dependency However conventional attention mechanism used memoryaugmented neural language model produce single output vector per time step vector used predicting next token well key value differentiable memory token history paper propose neural language model keyvalue attention mechanism output separate representation key value differentiable memory well encoding nextword distribution model outperforms existing memoryaugmented neural language model two corpus Yet found method mainly utilizes memory five recent output representation led unexpected main finding much simpler model based concatenation recent output representation previous time step par sophisticated memoryaugmented neural language model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhouhan Lin', 'Minwei Feng', 'Cicero Nogueira dos Santos', 'Mo Yu', 'Bing Xiang', 'Bowen Zhou', 'Yoshua Bengio']

1703.03130v1

This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model. As a side effect, the embedding comes with an easy way of visualizing what specific parts of the sentence are encoded into the embedding. We evaluate our model on 3 different tasks: author profiling, sentiment classification, and textual entailment. Results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks.

A Structured Self-attentive Sentence Embedding

http://arxiv.org/pdf/1703.03130v1

Title Structured Selfattentive Sentence Embedding Summary paper proposes new model extracting interpretable sentence embedding introducing selfattention Instead using vector use 2D matrix represent embedding row matrix attending different part sentence also propose selfattention mechanism special regularization term model side effect embedding come easy way visualizing specific part sentence encoded embedding evaluate model 3 different task author profiling sentiment classification textual entailment Results show model yield significant performance gain compared sentence embedding method 3 task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Samuel Rönnqvist', 'Niko Schenk', 'Christian Chiarcos']

1704.08092v1

We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model's ability to selectively focus on the relevant parts of an input sequence.

A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations

http://arxiv.org/pdf/1704.08092v1

Title Recurrent Neural Model Attention Recognition Chinese Implicit Discourse Relations Summary introduce attentionbased BiLSTM Chinese implicit discourse relation demonstrate modeling argument pair joint sequence outperform word orderagnostic approach model benefit partial sampling scheme conceptually simple yet achieves stateoftheart performance Chinese Discourse Treebank also visualize attention activity illustrate model ability selectively focus relevant part input sequence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lara J. Martin', 'Prithviraj Ammanabrolu', 'Xinyu Wang', 'William Hancock', 'Shruti Singh', 'Brent Harrison', 'Mark O. Riedl']

1706.01331v3

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories. We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity. We present a technique for preprocessing textual story data into event sequences. We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence). We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.

Event Representations for Automated Story Generation with Deep Neural Nets

http://arxiv.org/pdf/1706.01331v3

Title Event Representations Automated Story Generation Deep Neural Nets Summary Automated story generation problem automatically selecting sequence event action word told story seek develop system generate story learning everything need know textual story corpus date recurrent neural network learn language model character word sentence level little success generating coherent story explore question event representation provide midlevel abstraction word sentence order retain semantic information original data minimizing event sparsity present technique preprocessing textual story data event sequence present technique automated story generation whereby decompose problem generation successive event event2event generation natural language sentence event event2sentence give empirical result comparing different event representation effect event successor generation translation event natural language Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tong Wang', 'Xingdi Yuan', 'Adam Trischler']

1706.01450v1

We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequence-to-sequence framework that encodes the document and generates a question (answer) given an answer (question). Significant improvement in model performance is observed empirically on the SQuAD corpus, confirming our hypothesis that the model benefits from jointly learning to perform both tasks. We believe the joint model's novelty offers a new perspective on machine comprehension beyond architectural engineering, and serves as a first step towards autonomous information seeking.

A Joint Model for Question Answering and Question Generation

http://arxiv.org/pdf/1706.01450v1

Title Joint Model Question Answering Question Generation Summary propose generative machine comprehension model learns jointly ask answer question based document proposed model us sequencetosequence framework encodes document generates question answer given answer question Significant improvement model performance observed empirically SQuAD corpus confirming hypothesis model benefit jointly learning perform task believe joint model novelty offer new perspective machine comprehension beyond architectural engineering serf first step towards autonomous information seeking Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Wei Wen', 'Yuxiong He', 'Samyam Rajbhandari', 'Minjia Zhang', 'Wenhan Wang', 'Fang Liu', 'Bin Hu', 'Yiran Chen', 'Hai Li']

1709.05027v7

Model compression is significant for the wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and business clusters requiring quick responses to large-scale service requests. This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden states, cell states and outputs. Independently reducing the sizes of basic structures can result in inconsistent dimensions among them, and consequently, end up with invalid LSTM units. To overcome the problem, we propose Intrinsic Sparse Structures (ISS) in LSTMs. Removing a component of ISS will simultaneously decrease the sizes of all basic structures by one and thereby always maintain the dimension consistency. By learning ISS within LSTM units, the obtained LSTMs remain regular while having much smaller basic structures. Based on group Lasso regularization, our method achieves 10.59x speedup without losing any perplexity of a language modeling of Penn TreeBank dataset. It is also successfully evaluated through a compact model with only 2.69M weights for machine Question Answering of SQuAD dataset. Our approach is successfully extended to non- LSTM RNNs, like Recurrent Highway Networks (RHNs). Our source code is publicly available at https://github.com/wenwei202/iss-rnns

Learning Intrinsic Sparse Structures within Long Short-Term Memory

http://arxiv.org/pdf/1709.05027v7

Title Learning Intrinsic Sparse Structures within Long ShortTerm Memory Summary Model compression significant wide adoption Recurrent Neural Networks RNNs user device possessing limited resource business cluster requiring quick response largescale service request work aim learn structurallysparse Long ShortTerm Memory LSTM reducing size basic structure within LSTM unit including input update gate hidden state cell state output Independently reducing size basic structure result inconsistent dimension among consequently end invalid LSTM unit overcome problem propose Intrinsic Sparse Structures ISS LSTMs Removing component ISS simultaneously decrease size basic structure one thereby always maintain dimension consistency learning ISS within LSTM unit obtained LSTMs remain regular much smaller basic structure Based group Lasso regularization method achieves 1059x speedup without losing perplexity language modeling Penn TreeBank dataset also successfully evaluated compact model 269M weight machine Question Answering SQuAD dataset approach successfully extended non LSTM RNNs like Recurrent Highway Networks RHNs source code publicly available httpsgithubcomwenwei202issrnns Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Huda Hakami', 'Danushka Bollegala', 'Hayashi Kohei']

1709.06673v2

Representing the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, knowledge base completion and relational information retrieval. A simple, yet surprisingly accurate method for representing a relation between two words is to compute the vector offset (\PairDiff) between their corresponding word embeddings. Despite the empirical success, it remains unclear as to whether \PairDiff is the best operator for obtaining a relational representation from word embeddings. We conduct a theoretical analysis of generalised bilinear operators that can be used to measure the $\ell_{2}$ relational distance between two word-pairs. We show that, if the word embeddings are standardised and uncorrelated, such an operator will be independent of bilinear terms, and can be simplified to a linear form, where \PairDiff is a special case. For numerous word embedding types, we empirically verify the uncorrelation assumption, demonstrating the general applicability of our theoretical result. Moreover, we experimentally discover \PairDiff from the bilinear relation composition operator on several benchmark analogy datasets.

Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection

http://arxiv.org/pdf/1709.06673v2

Title PairDiff work Mathematical Analysis Bilinear Relational Compositional Operators Analogy Detection Summary Representing semantic relation exist two given word entity important first step widerange NLP application analogical reasoning knowledge base completion relational information retrieval simple yet surprisingly accurate method representing relation two word compute vector offset PairDiff corresponding word embeddings Despite empirical success remains unclear whether PairDiff best operator obtaining relational representation word embeddings conduct theoretical analysis generalised bilinear operator used measure ell2 relational distance two wordpairs show word embeddings standardised uncorrelated operator independent bilinear term simplified linear form PairDiff special case numerous word embedding type empirically verify uncorrelation assumption demonstrating general applicability theoretical result Moreover experimentally discover PairDiff bilinear relation composition operator several benchmark analogy datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhengdong Lu', 'Haotian Cui', 'Xianggen Liu', 'Yukun Yan', 'Daqi Zheng']

1709.08853v4

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.

Object-oriented Neural Programming (OONP) for Document Understanding

http://arxiv.org/pdf/1709.08853v4

Title Objectoriented Neural Programming OONP Document Understanding Summary propose Objectoriented Neural Programming OONP framework semantically parsing document specific domain Basically OONP read document par predesigned objectoriented data structure referred ontology paper reflects domainspecific semantics document OONP parser model semantic parsing decision process neural netbased Reader sequentially go document process build update intermediate ontology summarize partial understanding text cover OONP support rich family operation symbolic differentiable composing ontology big variety form symbolic differentiable representing state document OONP parser trained supervision different form strength including supervised learning SL reinforcement learning RL hybrid two experiment synthetic realworld document parsing task shown OONP learn handle fairly complicated ontology training data modest size Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Bin Bi', 'Hao Ma']

1709.10204v2

This paper proposes a novel neural machine reading model for open-domain question answering at scale. Existing machine comprehension models typically assume that a short piece of relevant text containing answers is already identified and given to the models, from which the models are designed to extract answers. This assumption, however, is not realistic for building a large-scale open-domain question answering system which requires both deep text understanding and identifying relevant text from corpus simultaneously. In this paper, we introduce Neural Comprehensive Ranker (NCR) that integrates both passage ranking and answer extraction in one single framework. A Q&A system based on this framework allows users to issue an open-domain question without needing to provide a piece of text that must contain the answer. Experiments show that the unified NCR model is able to outperform the states-of-the-art in both retrieval of relevant text and answer extraction.

A Neural Comprehensive Ranker (NCR) for Open-Domain Question Answering

http://arxiv.org/pdf/1709.10204v2

Title Neural Comprehensive Ranker NCR OpenDomain Question Answering Summary paper proposes novel neural machine reading model opendomain question answering scale Existing machine comprehension model typically assume short piece relevant text containing answer already identified given model model designed extract answer assumption however realistic building largescale opendomain question answering system requires deep text understanding identifying relevant text corpus simultaneously paper introduce Neural Comprehensive Ranker NCR integrates passage ranking answer extraction one single framework QA system based framework allows user issue opendomain question without needing provide piece text must contain answer Experiments show unified NCR model able outperform statesoftheart retrieval relevant text answer extraction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mirco Ravanelli', 'Philemon Brakel', 'Maurizio Omologo', 'Yoshua Bengio']

1710.00641v1

Speech recognition is largely taking advantage of deep learning, showing that substantial benefits can be obtained by modern Recurrent Neural Networks (RNNs). The most popular RNNs are Long Short-Term Memory (LSTMs), which typically reach state-of-the-art performance in many tasks thanks to their ability to learn long-term dependencies and robustness to vanishing gradients. Nevertheless, LSTMs have a rather complex design with three multiplicative gates, that might impair their efficient implementation. An attempt to simplify LSTMs has recently led to Gated Recurrent Units (GRUs), which are based on just two multiplicative gates. This paper builds on these efforts by further revising GRUs and proposing a simplified architecture potentially more suitable for speech recognition. The contribution of this work is two-fold. First, we suggest to remove the reset gate in the GRU design, resulting in a more efficient single-gate architecture. Second, we propose to replace tanh with ReLU activations in the state update equations. Results show that, in our implementation, the revised architecture reduces the per-epoch training time with more than 30% and consistently improves recognition performance across different tasks, input features, and noisy conditions when compared to a standard GRU.

Improving speech recognition by revising gated recurrent units

http://arxiv.org/pdf/1710.00641v1

Title Improving speech recognition revising gated recurrent unit Summary Speech recognition largely taking advantage deep learning showing substantial benefit obtained modern Recurrent Neural Networks RNNs popular RNNs Long ShortTerm Memory LSTMs typically reach stateoftheart performance many task thanks ability learn longterm dependency robustness vanishing gradient Nevertheless LSTMs rather complex design three multiplicative gate might impair efficient implementation attempt simplify LSTMs recently led Gated Recurrent Units GRUs based two multiplicative gate paper build effort revising GRUs proposing simplified architecture potentially suitable speech recognition contribution work twofold First suggest remove reset gate GRU design resulting efficient singlegate architecture Second propose replace tanh ReLU activation state update equation Results show implementation revised architecture reduces perepoch training time 30 consistently improves recognition performance across different task input feature noisy condition compared standard GRU Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Baolin Peng', 'Xiujun Li', 'Jianfeng Gao', 'Jingjing Liu', 'Kam-Fai Wong']

1801.06176v1

Training a task-completion dialogue agent with real users via reinforcement learning (RL) could be prohibitively expensive, because it requires many interactions with users. One alternative is to resort to a user simulator, while the discrepancy of between simulated and real users makes the learned policy unreliable in practice. This paper addresses these challenges by integrating planning into the dialogue policy learning based on Dyna-Q framework, and provides a more sample-efficient approach to learn the dialogue polices. The proposed agent consists of a planner trained on-line with limited real user experience that can generate large amounts of simulated experience to supplement with limited real user experience, and a policy model trained on these hybrid experiences. The effectiveness of our approach is validated on a movie-booking task in both a simulation setting and a human-in-the-loop setting.

Integrating planning for task-completion dialogue policy learning

http://arxiv.org/pdf/1801.06176v1

Title Integrating planning taskcompletion dialogue policy learning Summary Training taskcompletion dialogue agent real user via reinforcement learning RL could prohibitively expensive requires many interaction user One alternative resort user simulator discrepancy simulated real user make learned policy unreliable practice paper address challenge integrating planning dialogue policy learning based DynaQ framework provides sampleefficient approach learn dialogue police proposed agent consists planner trained online limited real user experience generate large amount simulated experience supplement limited real user experience policy model trained hybrid experience effectiveness approach validated moviebooking task simulation setting humanintheloop setting Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Andrew L. Maas', 'Peng Qi', 'Ziang Xie', 'Awni Y. Hannun', 'Christopher T. Lengerich', 'Daniel Jurafsky', 'Andrew Y. Ng']

1406.7806v2

Deep neural networks (DNNs) are now a central component of nearly all state-of-the-art speech recognition systems. Building neural network acoustic models requires several design decisions including network architecture, size, and training loss function. This paper offers an empirical investigation on which aspects of DNN acoustic model design are most important for speech recognition system performance. We report DNN classifier performance and final speech recognizer word error rates, and compare DNNs using several metrics to quantify factors influencing differences in task performance. Our first set of experiments use the standard Switchboard benchmark corpus, which contains approximately 300 hours of conversational telephone speech. We compare standard DNNs to convolutional networks, and present the first experiments using locally-connected, untied neural networks for acoustic modeling. We additionally build systems on a corpus of 2,100 hours of training data by combining the Switchboard and Fisher corpora. This larger corpus allows us to more thoroughly examine performance of large DNN models -- with up to ten times more parameters than those typically used in speech recognition systems. Our results suggest that a relatively simple DNN architecture and optimization technique produces strong results. These findings, along with previous work, help establish a set of best practices for building DNN hybrid speech recognition systems with maximum likelihood training. Our experiments in DNN optimization additionally serve as a case study for training DNNs with discriminative loss functions for speech tasks, as well as DNN classifiers more generally.

Building DNN Acoustic Models for Large Vocabulary Speech Recognition

http://arxiv.org/pdf/1406.7806v2

Title Building DNN Acoustic Models Large Vocabulary Speech Recognition Summary Deep neural network DNNs central component nearly stateoftheart speech recognition system Building neural network acoustic model requires several design decision including network architecture size training loss function paper offer empirical investigation aspect DNN acoustic model design important speech recognition system performance report DNN classifier performance final speech recognizer word error rate compare DNNs using several metric quantify factor influencing difference task performance first set experiment use standard Switchboard benchmark corpus contains approximately 300 hour conversational telephone speech compare standard DNNs convolutional network present first experiment using locallyconnected untied neural network acoustic modeling additionally build system corpus 2100 hour training data combining Switchboard Fisher corpus larger corpus allows u thoroughly examine performance large DNN model ten time parameter typically used speech recognition system result suggest relatively simple DNN architecture optimization technique produce strong result finding along previous work help establish set best practice building DNN hybrid speech recognition system maximum likelihood training experiment DNN optimization additionally serve case study training DNNs discriminative loss function speech task well DNN classifier generally Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['William Chan', 'Ian Lane']

1504.01482v1

We present a novel deep Recurrent Neural Network (RNN) model for acoustic modelling in Automatic Speech Recognition (ASR). We term our contribution as a TC-DNN-BLSTM-DNN model, the model combines a Deep Neural Network (DNN) with Time Convolution (TC), followed by a Bidirectional Long Short-Term Memory (BLSTM), and a final DNN. The first DNN acts as a feature processor to our model, the BLSTM then generates a context from the sequence acoustic signal, and the final DNN takes the context and models the posterior probabilities of the acoustic states. We achieve a 3.47 WER on the Wall Street Journal (WSJ) eval92 task or more than 8% relative improvement over the baseline DNN models.

Deep Recurrent Neural Networks for Acoustic Modelling

http://arxiv.org/pdf/1504.01482v1

Title Deep Recurrent Neural Networks Acoustic Modelling Summary present novel deep Recurrent Neural Network RNN model acoustic modelling Automatic Speech Recognition ASR term contribution TCDNNBLSTMDNN model model combine Deep Neural Network DNN Time Convolution TC followed Bidirectional Long ShortTerm Memory BLSTM final DNN first DNN act feature processor model BLSTM generates context sequence acoustic signal final DNN take context model posterior probability acoustic state achieve 347 WER Wall Street Journal WSJ eval92 task 8 relative improvement baseline DNN model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['David Krueger', 'Roland Memisevic']

1511.08400v7

We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states' norms. This penalty term is an effective regularizer for RNNs including LSTMs and IRNNs, improving performance on character-level language modeling and phoneme recognition, and outperforming weight noise and dropout. We achieve competitive performance (18.6\% PER) on the TIMIT phoneme recognition task for RNNs evaluated without beam search or an RNN transducer. With this penalty term, IRNN can achieve similar performance to LSTM on language modeling, although adding the penalty term to the LSTM results in superior performance. Our penalty term also prevents the exponential growth of IRNN's activations outside of their training horizon, allowing them to generalize to much longer sequences.

Regularizing RNNs by Stabilizing Activations

http://arxiv.org/pdf/1511.08400v7

Title Regularizing RNNs Stabilizing Activations Summary stabilize activation Recurrent Neural Networks RNNs penalizing squared distance successive hidden state norm penalty term effective regularizer RNNs including LSTMs IRNNs improving performance characterlevel language modeling phoneme recognition outperforming weight noise dropout achieve competitive performance 186 PER TIMIT phoneme recognition task RNNs evaluated without beam search RNN transducer penalty term IRNN achieve similar performance LSTM language modeling although adding penalty term LSTM result superior performance penalty term also prevents exponential growth IRNNs activation outside training horizon allowing generalize much longer sequence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Noam Shazeer', 'Azalia Mirhoseini', 'Krzysztof Maziarz', 'Andy Davis', 'Quoc Le', 'Geoffrey Hinton', 'Jeff Dean']

1701.06538v1

The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice, however, there are significant algorithmic and performance challenges. In this work, we address these challenges and finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example. We apply the MoE to the tasks of language modeling and machine translation, where model capacity is critical for absorbing the vast quantities of knowledge available in the training corpora. We present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost.

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

http://arxiv.org/pdf/1701.06538v1

Title Outrageously Large Neural Networks SparselyGated MixtureofExperts Layer Summary capacity neural network absorb information limited number parameter Conditional computation part network active perexample basis proposed theory way dramatically increasing model capacity without proportional increase computation practice however significant algorithmic performance challenge work address challenge finally realize promise conditional computation achieving greater 1000x improvement model capacity minor loss computational efficiency modern GPU cluster introduce SparselyGated MixtureofExperts layer MoE consisting thousand feedforward subnetworks trainable gating network determines sparse combination expert use example apply MoE task language modeling machine translation model capacity critical absorbing vast quantity knowledge available training corpus present model architecture MoE 137 billion parameter applied convolutionally stacked LSTM layer large language modeling machine translation benchmark model achieve significantly better result stateoftheart lower computational cost Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yacine Jernite', 'Samuel R. Bowman', 'David Sontag']

1705.00557v1

This work presents a novel objective function for the unsupervised training of neural network sentence encoders. It exploits signals from paragraph-level discourse coherence to train these models to understand text. Our objective is purely discriminative, allowing us to train models many times faster than was possible under prior methods, and it yields models which perform well in extrinsic evaluations.

Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning

http://arxiv.org/pdf/1705.00557v1

Title DiscourseBased Objectives Fast Unsupervised Sentence Representation Learning Summary work present novel objective function unsupervised training neural network sentence encoders exploit signal paragraphlevel discourse coherence train model understand text objective purely discriminative allowing u train model many time faster possible prior method yield model perform well extrinsic evaluation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhengyang Wang', 'Shuiwang Ji']

1705.06824v1

Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for modeling images is similar to traditional computer vision tasks, such as object recognition and image classification, visual question answering raises a different need for textual representation as compared to other natural language processing tasks. In this work, we perform a detailed analysis on natural language questions in visual question answering. Based on the analysis, we propose to rely on convolutional neural networks for learning textual representations. By exploring the various properties of convolutional neural networks specialized for text data, such as width and depth, we present our "CNN Inception + Gate" model. We show that our model improves question representations and thus the overall accuracy of visual question answering models. We also show that the text representation requirement in visual question answering is more complicated and comprehensive than that in conventional natural language processing tasks, making it a better task to evaluate textual representation methods. Shallow models like fastText, which can obtain comparable results with deep learning models in tasks like text classification, are not suitable in visual question answering.

Learning Convolutional Text Representations for Visual Question Answering

http://arxiv.org/pdf/1705.06824v1

Title Learning Convolutional Text Representations Visual Question Answering Summary Visual question answering recently proposed artificial intelligence task requires deep understanding image text deep learning image typically modeled convolutional neural network text typically modeled recurrent neural network requirement modeling image similar traditional computer vision task object recognition image classification visual question answering raise different need textual representation compared natural language processing task work perform detailed analysis natural language question visual question answering Based analysis propose rely convolutional neural network learning textual representation exploring various property convolutional neural network specialized text data width depth present CNN Inception Gate model show model improves question representation thus overall accuracy visual question answering model also show text representation requirement visual question answering complicated comprehensive conventional natural language processing task making better task evaluate textual representation method Shallow model like fastText obtain comparable result deep learning model task like text classification suitable visual question answering Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kyunghyun Cho', 'Bart van Merrienboer', 'Caglar Gulcehre', 'Dzmitry Bahdanau', 'Fethi Bougares', 'Holger Schwenk', 'Yoshua Bengio']

1406.1078v3

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

http://arxiv.org/pdf/1406.1078v3

Title Learning Phrase Representations using RNN EncoderDecoder Statistical Machine Translation Summary paper propose novel neural network model called RNN EncoderDecoder consists two recurrent neural network RNN One RNN encodes sequence symbol fixedlength vector representation decodes representation another sequence symbol encoder decoder proposed model jointly trained maximize conditional probability target sequence given source sequence performance statistical machine translation system empirically found improve using conditional probability phrase pair computed RNN EncoderDecoder additional feature existing loglinear model Qualitatively show proposed model learns semantically syntactically meaningful representation linguistic phrase Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhiyuan Tang', 'Dong Wang', 'Zhiyong Zhang']

1505.04630v5

Recurrent neural networks (RNNs), particularly long short-term memory (LSTM), have gained much attention in automatic speech recognition (ASR). Although some successful stories have been reported, training RNNs remains highly challenging, especially with limited training data. Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predictions generated by the teacher model as supervision. This knowledge transfer learning has been employed to train simple neural nets with a complex one, so that the final performance can reach a level that is infeasible to obtain by regular training. In this paper, we employ the knowledge transfer learning approach to train RNNs (precisely LSTM) using a deep neural network (DNN) model as the teacher. This is different from most of the existing research on knowledge transfer learning, since the teacher (DNN) is assumed to be weaker than the child (RNN); however, our experiments on an ASR task showed that it works fairly well: without applying any tricks on the learning scheme, this approach can train RNNs successfully even with limited training data.

Recurrent Neural Network Training with Dark Knowledge Transfer

http://arxiv.org/pdf/1505.04630v5

Title Recurrent Neural Network Training Dark Knowledge Transfer Summary Recurrent neural network RNNs particularly long shortterm memory LSTM gained much attention automatic speech recognition ASR Although successful story reported training RNNs remains highly challenging especially limited training data Recent research found welltrained model used teacher train child model using prediction generated teacher model supervision knowledge transfer learning employed train simple neural net complex one final performance reach level infeasible obtain regular training paper employ knowledge transfer learning approach train RNNs precisely LSTM using deep neural network DNN model teacher different existing research knowledge transfer learning since teacher DNN assumed weaker child RNN however experiment ASR task showed work fairly well without applying trick learning scheme approach train RNNs successfully even limited training data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Haşim Sak', 'Andrew Senior', 'Françoise Beaufays']

1402.1128v1

Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designed to address the vanishing and exploding gradient problems of conventional RNNs. Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences. They have been successfully used for sequence labeling and sequence prediction tasks, such as handwriting recognition, language modeling, phonetic labeling of acoustic frames. However, in contrast to the deep neural networks, the use of RNNs in speech recognition has been limited to phone recognition in small scale tasks. In this paper, we present novel LSTM based RNN architectures which make more effective use of model parameters to train acoustic models for large vocabulary speech recognition. We train and compare LSTM, RNN and DNN models at various numbers of parameters and configurations. We show that LSTM models converge quickly and give state of the art speech recognition performance for relatively small sized models.

Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition

http://arxiv.org/pdf/1402.1128v1

Title Long ShortTerm Memory Based Recurrent Neural Network Architectures Large Vocabulary Speech Recognition Summary Long ShortTerm Memory LSTM recurrent neural network RNN architecture designed address vanishing exploding gradient problem conventional RNNs Unlike feedforward neural network RNNs cyclic connection making powerful modeling sequence successfully used sequence labeling sequence prediction task handwriting recognition language modeling phonetic labeling acoustic frame However contrast deep neural network use RNNs speech recognition limited phone recognition small scale task paper present novel LSTM based RNN architecture make effective use model parameter train acoustic model large vocabulary speech recognition train compare LSTM RNN DNN model various number parameter configuration show LSTM model converge quickly give state art speech recognition performance relatively small sized model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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