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Dynamic Time Lag Regression: Predicting What & When | 9 | iclr | 1 | 7 | 2023-06-18 09:10:06.100000 | https://github.com/transcendent-ai-labs/PlasmaML | 16 | Dynamic Time Lag Regression: Predicting What and When | https://scholar.google.com/scholar?cluster=5170552035479326246&hl=en&as_sdt=0,37 | 7 | 2,020 |
Unpaired Point Cloud Completion on Real Scans using Adversarial Training | 96 | iclr | 11 | 1 | 2023-06-18 09:10:06.302000 | https://github.com/xuelin-chen/pcl2pcl-gan-pub | 81 | Unpaired point cloud completion on real scans using adversarial training | https://scholar.google.com/scholar?cluster=6319477762897752803&hl=en&as_sdt=0,5 | 7 | 2,020 |
Selection via Proxy: Efficient Data Selection for Deep Learning | 133 | iclr | 19 | 1 | 2023-06-18 09:10:06.506000 | https://github.com/stanford-futuredata/selection-via-proxy | 78 | Selection via proxy: Efficient data selection for deep learning | https://scholar.google.com/scholar?cluster=10606664093807319412&hl=en&as_sdt=0,32 | 8 | 2,020 |
Global Relational Models of Source Code | 194 | iclr | 20 | 3 | 2023-06-18 09:10:06.708000 | https://github.com/VHellendoorn/ICLR20-Great | 79 | Global relational models of source code | https://scholar.google.com/scholar?cluster=5949441341653621917&hl=en&as_sdt=0,5 | 4 | 2,020 |
Adversarially robust transfer learning | 103 | iclr | 2 | 1 | 2023-06-18 09:10:06.912000 | https://github.com/ashafahi/RobustTransferLWF | 16 | Adversarially robust transfer learning | https://scholar.google.com/scholar?cluster=247907928453605112&hl=en&as_sdt=0,47 | 4 | 2,020 |
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness | 119 | iclr | 6 | 1 | 2023-06-18 09:10:07.115000 | https://github.com/IBM/model-sanitization | 22 | Bridging mode connectivity in loss landscapes and adversarial robustness | https://scholar.google.com/scholar?cluster=14988732432147772285&hl=en&as_sdt=0,33 | 7 | 2,020 |
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples | 491 | iclr | 136 | 44 | 2023-06-18 09:10:07.317000 | https://github.com/google-research/meta-dataset | 698 | Meta-dataset: A dataset of datasets for learning to learn from few examples | https://scholar.google.com/scholar?cluster=14266702502378757393&hl=en&as_sdt=0,32 | 24 | 2,020 |
Deep Imitative Models for Flexible Inference, Planning, and Control | 124 | iclr | 14 | 19 | 2023-06-18 09:10:07.521000 | https://github.com/nrhine1/deep_imitative_models | 68 | Deep imitative models for flexible inference, planning, and control | https://scholar.google.com/scholar?cluster=599185864570432210&hl=en&as_sdt=0,45 | 3 | 2,020 |
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning | 75 | iclr | 10 | 0 | 2023-06-18 09:10:07.723000 | https://github.com/011235813/cm3 | 47 | Cm3: Cooperative multi-goal multi-stage multi-agent reinforcement learning | https://scholar.google.com/scholar?cluster=11188676090053014781&hl=en&as_sdt=0,3 | 3 | 2,020 |
Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks | 84 | iclr | 9 | 3 | 2023-06-18 09:10:07.925000 | https://github.com/LabForComputationalVision/bias_free_denoising | 36 | Robust and interpretable blind image denoising via bias-free convolutional neural networks | https://scholar.google.com/scholar?cluster=11707547899272178627&hl=en&as_sdt=0,36 | 5 | 2,020 |
DeepV2D: Video to Depth with Differentiable Structure from Motion | 146 | iclr | 89 | 28 | 2023-06-18 09:10:08.128000 | https://github.com/princeton-vl/DeepV2D | 598 | Deepv2d: Video to depth with differentiable structure from motion | https://scholar.google.com/scholar?cluster=564045569449021652&hl=en&as_sdt=0,33 | 20 | 2,020 |
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack | 142 | iclr | 27 | 10 | 2023-06-18 09:10:08.331000 | https://github.com/cmhcbb/attackbox | 50 | Sign-opt: A query-efficient hard-label adversarial attack | https://scholar.google.com/scholar?cluster=4337120578340154737&hl=en&as_sdt=0,5 | 5 | 2,020 |
Fast is better than free: Revisiting adversarial training | 869 | iclr | 92 | 2 | 2023-06-18 09:10:08.534000 | https://github.com/locuslab/fast_adversarial | 385 | Fast is better than free: Revisiting adversarial training | https://scholar.google.com/scholar?cluster=227717459026762223&hl=en&as_sdt=0,6 | 12 | 2,020 |
DBA: Distributed Backdoor Attacks against Federated Learning | 377 | iclr | 37 | 4 | 2023-06-18 09:10:08.737000 | https://github.com/AI-secure/DBA | 134 | Dba: Distributed backdoor attacks against federated learning | https://scholar.google.com/scholar?cluster=12314378493827075057&hl=en&as_sdt=0,1 | 2 | 2,020 |
DeFINE: Deep Factorized Input Token Embeddings for Neural Sequence Modeling | 19 | iclr | 50 | 7 | 2023-06-18 09:10:08.941000 | https://github.com/sacmehta/delight | 443 | Define: Deep factorized input token embeddings for neural sequence modeling | https://scholar.google.com/scholar?cluster=1535018014104631427&hl=en&as_sdt=0,29 | 14 | 2,020 |
Learning to solve the credit assignment problem | 53 | iclr | 0 | 0 | 2023-06-18 09:10:09.143000 | https://github.com/benlansdell/synthfeedback | 3 | Learning to solve the credit assignment problem | https://scholar.google.com/scholar?cluster=1954938718512669715&hl=en&as_sdt=0,37 | 5 | 2,020 |
Four Things Everyone Should Know to Improve Batch Normalization | 48 | iclr | 1 | 1 | 2023-06-18 09:10:09.347000 | https://github.com/ceciliaresearch/four_things_batch_norm | 20 | Four things everyone should know to improve batch normalization | https://scholar.google.com/scholar?cluster=8831824515210942226&hl=en&as_sdt=0,5 | 1 | 2,020 |
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving | 312 | iclr | 116 | 18 | 2023-06-18 09:10:09.551000 | https://github.com/mileyan/Pseudo_Lidar_V2 | 539 | Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving | https://scholar.google.com/scholar?cluster=10904480408184954283&hl=en&as_sdt=0,10 | 40 | 2,020 |
Learning to Learn by Zeroth-Order Oracle | 14 | iclr | 5 | 0 | 2023-06-18 09:10:09.753000 | https://github.com/RYoungJ/ZO-L2L | 13 | Learning to learn by zeroth-order oracle | https://scholar.google.com/scholar?cluster=8954748594282159172&hl=en&as_sdt=0,31 | 2 | 2,020 |
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames | 273 | iclr | 378 | 170 | 2023-06-18 09:10:09.955000 | https://github.com/facebookresearch/habitat-api | 1,109 | Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames | https://scholar.google.com/scholar?cluster=4884965845219755657&hl=en&as_sdt=0,6 | 43 | 2,020 |
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction | 38 | iclr | 1 | 0 | 2023-06-18 09:10:10.159000 | https://github.com/sangdon/PAC-confidence-set | 5 | PAC confidence sets for deep neural networks via calibrated prediction | https://scholar.google.com/scholar?cluster=13464804698510313899&hl=en&as_sdt=0,5 | 2 | 2,020 |
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations | 19 | iclr | 12 | 3 | 2023-06-18 09:10:10.362000 | https://github.com/cornell-zhang/dnn-gating | 69 | Precision gating: Improving neural network efficiency with dynamic dual-precision activations | https://scholar.google.com/scholar?cluster=5604094105865350488&hl=en&as_sdt=0,39 | 9 | 2,020 |
Oblique Decision Trees from Derivatives of ReLU Networks | 12 | iclr | 7 | 1 | 2023-06-18 09:10:10.564000 | https://github.com/guanghelee/iclr20-lcn | 20 | Oblique decision trees from derivatives of relu networks | https://scholar.google.com/scholar?cluster=15458108821420666095&hl=en&as_sdt=0,31 | 4 | 2,020 |
Learn to Explain Efficiently via Neural Logic Inductive Learning | 58 | iclr | 17 | 3 | 2023-06-18 09:10:10.768000 | https://github.com/gblackout/NLIL | 38 | Learn to explain efficiently via neural logic inductive learning | https://scholar.google.com/scholar?cluster=4550874980727321525&hl=en&as_sdt=0,15 | 4 | 2,020 |
Improved memory in recurrent neural networks with sequential non-normal dynamics | 12 | iclr | 2 | 0 | 2023-06-18 09:10:10.971000 | https://github.com/eminorhan/nonnormal-init | 3 | Improved memory in recurrent neural networks with sequential non-normal dynamics | https://scholar.google.com/scholar?cluster=2472327505855554396&hl=en&as_sdt=0,26 | 3 | 2,020 |
Neural Module Networks for Reasoning over Text | 121 | iclr | 14 | 3 | 2023-06-18 09:10:11.174000 | https://github.com/nitishgupta/nmn-drop | 120 | Neural module networks for reasoning over text | https://scholar.google.com/scholar?cluster=2046532742306416986&hl=en&as_sdt=0,5 | 11 | 2,020 |
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling | 2 | iclr | 1 | 1 | 2023-06-18 09:10:11.377000 | https://github.com/BoChenGroup/VHE-GAN | 9 | Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling | https://scholar.google.com/scholar?cluster=6283375856940214417&hl=en&as_sdt=0,5 | 2 | 2,020 |
Towards Fast Adaptation of Neural Architectures with Meta Learning | 70 | iclr | 7 | 2 | 2023-06-18 09:10:11.580000 | https://github.com/dongzelian/T-NAS | 27 | Towards fast adaptation of neural architectures with meta learning | https://scholar.google.com/scholar?cluster=2375275580093901945&hl=en&as_sdt=0,5 | 3 | 2,020 |
Graph Constrained Reinforcement Learning for Natural Language Action Spaces | 83 | iclr | 13 | 1 | 2023-06-18 09:10:11.783000 | https://github.com/rajammanabrolu/KG-A2C | 54 | Graph constrained reinforcement learning for natural language action spaces | https://scholar.google.com/scholar?cluster=15066208654437399788&hl=en&as_sdt=0,5 | 2 | 2,020 |
BERTScore: Evaluating Text Generation with BERT | 2,078 | iclr | 186 | 12 | 2023-06-18 09:10:11.986000 | https://github.com/Tiiiger/bert_score | 1,161 | Bertscore: Evaluating text generation with bert | https://scholar.google.com/scholar?cluster=5304773001741994283&hl=en&as_sdt=0,5 | 22 | 2,020 |
Composition-based Multi-Relational Graph Convolutional Networks | 533 | iclr | 102 | 13 | 2023-06-18 09:10:12.190000 | https://github.com/malllabiisc/CompGCN | 545 | Composition-based multi-relational graph convolutional networks | https://scholar.google.com/scholar?cluster=4927480689371858635&hl=en&as_sdt=0,5 | 17 | 2,020 |
Gradient-Based Neural DAG Learning | 150 | iclr | 19 | 2 | 2023-06-18 09:10:12.393000 | https://github.com/kurowasan/GraN-DAG | 78 | Gradient-based neural dag learning | https://scholar.google.com/scholar?cluster=10487378596908501013&hl=en&as_sdt=0,10 | 6 | 2,020 |
The Local Elasticity of Neural Networks | 29 | iclr | 2 | 1 | 2023-06-18 09:10:12.596000 | https://github.com/HornHehhf/LocalElasticity | 6 | The local elasticity of neural networks | https://scholar.google.com/scholar?cluster=2497659647078092985&hl=en&as_sdt=0,38 | 3 | 2,020 |
Convergence of Gradient Methods on Bilinear Zero-Sum Games | 33 | iclr | 1 | 0 | 2023-06-18 09:10:12.799000 | https://github.com/Gordon-Guojun-Zhang/ICLR-2020 | 1 | Convergence of gradient methods on bilinear zero-sum games | https://scholar.google.com/scholar?cluster=18092221422699658079&hl=en&as_sdt=0,31 | 2 | 2,020 |
Learning from Explanations with Neural Execution Tree | 33 | iclr | 4 | 0 | 2023-06-18 09:10:13.002000 | https://github.com/INK-USC/NExT | 18 | Learning from explanations with neural execution tree | https://scholar.google.com/scholar?cluster=7878469874238216625&hl=en&as_sdt=0,5 | 6 | 2,020 |
Jelly Bean World: A Testbed for Never-Ending Learning | 19 | iclr | 14 | 2 | 2023-06-18 09:10:13.205000 | https://github.com/eaplatanios/jelly-bean-world | 68 | Jelly bean world: A testbed for never-ending learning | https://scholar.google.com/scholar?cluster=13920710483001851413&hl=en&as_sdt=0,5 | 6 | 2,020 |
Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality | 32 | iclr | 3 | 0 | 2023-06-18 09:10:13.408000 | https://github.com/sakhanna/SRU_for_GCI | 21 | Economy statistical recurrent units for inferring nonlinear granger causality | https://scholar.google.com/scholar?cluster=9739971127623592335&hl=en&as_sdt=0,14 | 2 | 2,020 |
Bayesian Meta Sampling for Fast Uncertainty Adaptation | 17 | iclr | 3 | 0 | 2023-06-18 09:10:13.611000 | https://github.com/zheshiyige/meta-sampling | 8 | Bayesian meta sampling for fast uncertainty adaptation | https://scholar.google.com/scholar?cluster=15645160927746258341&hl=en&as_sdt=0,5 | 1 | 2,020 |
Non-Autoregressive Dialog State Tracking | 49 | iclr | 3 | 2 | 2023-06-18 09:10:13.814000 | https://github.com/henryhungle/NADST | 45 | Non-autoregressive dialog state tracking | https://scholar.google.com/scholar?cluster=13522465904465807685&hl=en&as_sdt=0,5 | 5 | 2,020 |
RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients? | 40 | iclr | 1 | 0 | 2023-06-18 09:10:14.018000 | https://github.com/anilkagak2/TARNN | 6 | Rnns incrementally evolving on an equilibrium manifold: A panacea for vanishing and exploding gradients? | https://scholar.google.com/scholar?cluster=14548762609337726303&hl=en&as_sdt=0,5 | 3 | 2,020 |
The Early Phase of Neural Network Training | 128 | iclr | 106 | 15 | 2023-06-18 09:10:14.220000 | https://github.com/facebookresearch/open_lth | 590 | The early phase of neural network training | https://scholar.google.com/scholar?cluster=15707294236176535435&hl=en&as_sdt=0,5 | 57 | 2,020 |
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization | 37 | iclr | 25 | 0 | 2023-06-18 09:10:14.424000 | https://github.com/megvii-model/MABN | 182 | Towards stabilizing batch statistics in backward propagation of batch normalization | https://scholar.google.com/scholar?cluster=2467606863922912536&hl=en&as_sdt=0,5 | 8 | 2,020 |
Single Episode Policy Transfer in Reinforcement Learning | 27 | iclr | 3 | 0 | 2023-06-18 09:10:14.627000 | https://github.com/011235813/SEPT | 16 | Single episode policy transfer in reinforcement learning | https://scholar.google.com/scholar?cluster=2255040216539653326&hl=en&as_sdt=0,14 | 5 | 2,020 |
Generalization through Memorization: Nearest Neighbor Language Models | 360 | iclr | 41 | 4 | 2023-06-18 09:10:14.830000 | https://github.com/urvashik/knnlm | 253 | Generalization through memorization: Nearest neighbor language models | https://scholar.google.com/scholar?cluster=17433739628027955410&hl=en&as_sdt=0,5 | 7 | 2,020 |
Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention | 98 | iclr | 15 | 1 | 2023-06-18 09:10:15.034000 | https://github.com/microsoft/Transformer-XH | 67 | Transformer-xh: Multi-evidence reasoning with extra hop attention | https://scholar.google.com/scholar?cluster=1330946954324829338&hl=en&as_sdt=0,5 | 8 | 2,020 |
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks | 56 | iclr | 12 | 0 | 2023-06-18 09:10:15.236000 | https://github.com/facebookresearch/GAN-optimization-landscape | 31 | A closer look at the optimization landscapes of generative adversarial networks | https://scholar.google.com/scholar?cluster=8697338348379515621&hl=en&as_sdt=0,3 | 6 | 2,020 |
Revisiting Self-Training for Neural Sequence Generation | 191 | iclr | 8 | 2 | 2023-06-18 09:10:15.440000 | https://github.com/jxhe/self-training-text-generation | 45 | Revisiting self-training for neural sequence generation | https://scholar.google.com/scholar?cluster=7004703497998979134&hl=en&as_sdt=0,47 | 2 | 2,020 |
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators | 58 | iclr | 4 | 0 | 2023-06-18 09:10:15.643000 | https://github.com/MLI-lab/overparameterized_convolutional_generators | 14 | Denoising and regularization via exploiting the structural bias of convolutional generators | https://scholar.google.com/scholar?cluster=11773092557321050875&hl=en&as_sdt=0,5 | 4 | 2,020 |
LambdaNet: Probabilistic Type Inference using Graph Neural Networks | 88 | iclr | 12 | 0 | 2023-06-18 09:10:15.846000 | https://github.com/MrVPlusOne/LambdaNet | 42 | Lambdanet: Probabilistic type inference using graph neural networks | https://scholar.google.com/scholar?cluster=14484091760382594314&hl=en&as_sdt=0,5 | 9 | 2,020 |
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping | 22 | iclr | 4 | 0 | 2023-06-18 09:10:16.050000 | https://github.com/aharley/neural_3d_mapping | 31 | Learning from unlabelled videos using contrastive predictive neural 3d mapping | https://scholar.google.com/scholar?cluster=7365572649342061474&hl=en&as_sdt=0,33 | 8 | 2,020 |
Decoupling Representation and Classifier for Long-Tailed Recognition | 786 | iclr | 117 | 13 | 2023-06-18 09:10:16.279000 | https://github.com/facebookresearch/classifier-balancing | 873 | Decoupling representation and classifier for long-tailed recognition | https://scholar.google.com/scholar?cluster=2236026226436038230&hl=en&as_sdt=0,41 | 21 | 2,020 |
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework | 61 | iclr | 10 | 1 | 2023-06-18 09:10:16.482000 | https://github.com/thespectrewithin/joint-align | 51 | Cross-lingual alignment vs joint training: A comparative study and a simple unified framework | https://scholar.google.com/scholar?cluster=17808816563200033029&hl=en&as_sdt=0,33 | 4 | 2,020 |
Uncertainty-guided Continual Learning with Bayesian Neural Networks | 165 | iclr | 11 | 8 | 2023-06-18 09:10:16.686000 | https://github.com/SaynaEbrahimi/UCB | 66 | Uncertainty-guided continual learning with bayesian neural networks | https://scholar.google.com/scholar?cluster=10082473234430355613&hl=en&as_sdt=0,39 | 4 | 2,020 |
Picking Winning Tickets Before Training by Preserving Gradient Flow | 378 | iclr | 11 | 1 | 2023-06-18 09:10:16.889000 | https://github.com/alecwangcq/GraSP | 91 | Picking winning tickets before training by preserving gradient flow | https://scholar.google.com/scholar?cluster=9466463567127487961&hl=en&as_sdt=0,10 | 2 | 2,020 |
Inductive representation learning on temporal graphs | 299 | iclr | 53 | 12 | 2023-06-18 09:10:17.092000 | https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs | 222 | Inductive representation learning on temporal graphs | https://scholar.google.com/scholar?cluster=6732351798905235278&hl=en&as_sdt=0,36 | 3 | 2,020 |
BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning | 287 | iclr | 79 | 73 | 2023-06-18 09:10:17.295000 | https://github.com/google/edward2 | 645 | Batchensemble: an alternative approach to efficient ensemble and lifelong learning | https://scholar.google.com/scholar?cluster=2684475579133602&hl=en&as_sdt=0,21 | 20 | 2,020 |
Towards neural networks that provably know when they don't know | 121 | iclr | 1 | 1 | 2023-06-18 09:10:17.498000 | https://github.com/AlexMeinke/certified-certain-uncertainty | 34 | Towards neural networks that provably know when they don't know | https://scholar.google.com/scholar?cluster=3907037768613550224&hl=en&as_sdt=0,5 | 5 | 2,020 |
Learning representations for binary-classification without backpropagation | 7 | iclr | 2 | 0 | 2023-06-18 09:10:17.702000 | https://github.com/mlech26l/iclr_paper_mdfa | 2 | Learning representations for binary-classification without backpropagation | https://scholar.google.com/scholar?cluster=6618144182532521283&hl=en&as_sdt=0,34 | 2 | 2,020 |
HiLLoC: lossless image compression with hierarchical latent variable models | 51 | iclr | 7 | 1 | 2023-06-18 09:10:17.915000 | https://github.com/hilloc-submission/hilloc | 34 | Hilloc: Lossless image compression with hierarchical latent variable models | https://scholar.google.com/scholar?cluster=8743808448385898182&hl=en&as_sdt=0,36 | 7 | 2,020 |
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation | 3 | iclr | 3 | 0 | 2023-06-18 09:10:18.119000 | https://github.com/xinjiefan/ACMC_ICLR | 4 | Adaptive correlated Monte Carlo for contextual categorical sequence generation | https://scholar.google.com/scholar?cluster=3786399280246105812&hl=en&as_sdt=0,15 | 4 | 2,020 |
PairNorm: Tackling Oversmoothing in GNNs | 371 | iclr | 11 | 4 | 2023-06-18 09:10:18.321000 | https://github.com/LingxiaoShawn/PairNorm | 68 | Pairnorm: Tackling oversmoothing in gnns | https://scholar.google.com/scholar?cluster=244277682967965047&hl=en&as_sdt=0,5 | 2 | 2,020 |
Controlling generative models with continuous factors of variations | 104 | iclr | 4 | 8 | 2023-06-18 09:10:18.524000 | https://github.com/AntoinePlumerault/Controlling-generative-models-with-continuous-factors-of-variations | 20 | Controlling generative models with continuous factors of variations | https://scholar.google.com/scholar?cluster=9062279682169095695&hl=en&as_sdt=0,5 | 2 | 2,020 |
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control | 211 | iclr | 12 | 0 | 2023-06-18 09:10:18.727000 | https://github.com/Physics-aware-AI/Symplectic-ODENet | 34 | Symplectic ode-net: Learning hamiltonian dynamics with control | https://scholar.google.com/scholar?cluster=16212087481734650197&hl=en&as_sdt=0,33 | 5 | 2,020 |
Quantum Algorithms for Deep Convolutional Neural Networks | 103 | iclr | 15 | 1 | 2023-06-18 09:10:18.929000 | https://github.com/JonasLandman/QCNN | 84 | Quantum algorithms for deep convolutional neural networks | https://scholar.google.com/scholar?cluster=6858802029383173289&hl=en&as_sdt=0,10 | 1 | 2,020 |
Deep Graph Matching Consensus | 175 | iclr | 45 | 4 | 2023-06-18 09:10:19.132000 | https://github.com/rusty1s/deep-graph-matching-consensus | 238 | Deep graph matching consensus | https://scholar.google.com/scholar?cluster=13831077548402480322&hl=en&as_sdt=0,33 | 9 | 2,020 |
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers | 77 | iclr | 5 | 1 | 2023-06-18 09:10:19.336000 | https://github.com/junjieliu2910/DynamicSaprseTraining | 27 | Dynamic sparse training: Find efficient sparse network from scratch with trainable masked layers | https://scholar.google.com/scholar?cluster=2417069645139449524&hl=en&as_sdt=0,5 | 3 | 2,020 |
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference | 72 | iclr | 7 | 1 | 2023-06-18 09:10:19.538000 | https://github.com/TAMU-VITA/triple-wins | 22 | Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference | https://scholar.google.com/scholar?cluster=16965650260059633977&hl=en&as_sdt=0,33 | 12 | 2,020 |
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation | 250 | iclr | 4 | 1 | 2023-06-18 09:10:19.741000 | https://github.com/DeepGraphLearning/GraphAF | 44 | Graphaf: a flow-based autoregressive model for molecular graph generation | https://scholar.google.com/scholar?cluster=2901334410635777038&hl=en&as_sdt=0,19 | 8 | 2,020 |
The Curious Case of Neural Text Degeneration | 1,564 | iclr | 13 | 2 | 2023-06-18 09:10:19.943000 | https://github.com/ari-holtzman/degen | 131 | The curious case of neural text degeneration | https://scholar.google.com/scholar?cluster=13091440005032798110&hl=en&as_sdt=0,33 | 5 | 2,020 |
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning | 85 | iclr | 1 | 1 | 2023-06-18 09:10:20.146000 | https://github.com/KDL-umass/saliency_maps | 9 | Exploratory not explanatory: Counterfactual analysis of saliency maps for deep reinforcement learning | https://scholar.google.com/scholar?cluster=6988064126122361563&hl=en&as_sdt=0,5 | 6 | 2,020 |
Guiding Program Synthesis by Learning to Generate Examples | 12 | iclr | 3 | 1 | 2023-06-18 09:10:20.349000 | https://github.com/eth-sri/guiding-synthesizers | 12 | Guiding program synthesis by learning to generate examples | https://scholar.google.com/scholar?cluster=5759998545534932408&hl=en&as_sdt=0,14 | 9 | 2,020 |
Once-for-All: Train One Network and Specialize it for Efficient Deployment | 930 | iclr | 309 | 55 | 2023-06-18 09:10:20.553000 | https://github.com/mit-han-lab/once-for-all | 1,676 | Once-for-all: Train one network and specialize it for efficient deployment | https://scholar.google.com/scholar?cluster=5004054402916064925&hl=en&as_sdt=0,47 | 53 | 2,020 |
Multi-Agent Interactions Modeling with Correlated Policies | 14 | iclr | 1 | 0 | 2023-06-18 09:10:20.755000 | https://github.com/apexrl/CoDAIL | 19 | Multi-agent interactions modeling with correlated policies | https://scholar.google.com/scholar?cluster=1707555896923900607&hl=en&as_sdt=0,11 | 4 | 2,020 |
PCMC-Net: Feature-based Pairwise Choice Markov Chains | 4 | iclr | 2 | 0 | 2023-06-18 09:10:20.958000 | https://github.com/alherit/PCMC-Net | 0 | PCMC-Net: Feature-based pairwise choice Markov chains | https://scholar.google.com/scholar?cluster=6364308783173808929&hl=en&as_sdt=0,5 | 2 | 2,020 |
Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings | 192 | iclr | 25 | 4 | 2023-06-18 09:10:21.161000 | https://github.com/hyren/query2box | 185 | Query2box: Reasoning over knowledge graphs in vector space using box embeddings | https://scholar.google.com/scholar?cluster=12162114509339906104&hl=en&as_sdt=0,23 | 5 | 2,020 |
Rethinking the Hyperparameters for Fine-tuning | 91 | iclr | 35 | 8 | 2023-06-18 09:10:21.364000 | https://github.com/richardaecn/cvpr18-inaturalist-transfer | 189 | Rethinking the hyperparameters for fine-tuning | https://scholar.google.com/scholar?cluster=14029720773108023404&hl=en&as_sdt=0,44 | 9 | 2,020 |
Plug and Play Language Models: A Simple Approach to Controlled Text Generation | 532 | iclr | 187 | 26 | 2023-06-18 09:10:21.567000 | https://github.com/uber-research/PPLM | 1,061 | Plug and play language models: A simple approach to controlled text generation | https://scholar.google.com/scholar?cluster=9850887597524341216&hl=en&as_sdt=0,5 | 29 | 2,020 |
Jacobian Adversarially Regularized Networks for Robustness | 59 | iclr | 0 | 2 | 2023-06-18 09:10:21.769000 | https://github.com/alvinchangw/JARN_ICLR2020 | 20 | Jacobian adversarially regularized networks for robustness | https://scholar.google.com/scholar?cluster=8296271536774350168&hl=en&as_sdt=0,5 | 3 | 2,020 |
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning | 69 | iclr | 24 | 15 | 2023-06-18 09:10:21.972000 | https://github.com/qian18long/epciclr2020 | 103 | Evolutionary population curriculum for scaling multi-agent reinforcement learning | https://scholar.google.com/scholar?cluster=13227492821855003720&hl=en&as_sdt=0,5 | 6 | 2,020 |
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators | 2,536 | iclr | 339 | 58 | 2023-06-18 09:10:22.175000 | https://github.com/google-research/electra | 2,195 | Electra: Pre-training text encoders as discriminators rather than generators | https://scholar.google.com/scholar?cluster=18273102803868155691&hl=en&as_sdt=0,22 | 61 | 2,020 |
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering | 245 | iclr | 66 | 2 | 2023-06-18 09:10:22.382000 | https://github.com/AkariAsai/learning_to_retrieve_reasoning_paths | 409 | Learning to retrieve reasoning paths over wikipedia graph for question answering | https://scholar.google.com/scholar?cluster=9983656712986759365&hl=en&as_sdt=0,5 | 18 | 2,020 |
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks | 51 | iclr | 7 | 2 | 2023-06-18 09:10:22.585000 | https://github.com/ml-research/pau | 53 | Pad\'e Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks | https://scholar.google.com/scholar?cluster=10060434819073628670&hl=en&as_sdt=0,5 | 6 | 2,020 |
Contrastive Representation Distillation | 731 | iclr | 352 | 34 | 2023-06-18 09:10:22.788000 | https://github.com/HobbitLong/RepDistiller | 1,829 | Contrastive representation distillation | https://scholar.google.com/scholar?cluster=11598873002614112751&hl=en&as_sdt=0,33 | 17 | 2,020 |
Certified Defenses for Adversarial Patches | 120 | iclr | 3 | 0 | 2023-06-18 09:10:22.992000 | https://github.com/Ping-C/certifiedpatchdefense | 30 | Certified defenses for adversarial patches | https://scholar.google.com/scholar?cluster=2964763599882748614&hl=en&as_sdt=0,5 | 2 | 2,020 |
Deep Symbolic Superoptimization Without Human Knowledge | 4 | iclr | 1 | 2 | 2023-06-18 09:10:23.195000 | https://github.com/shihui2010/symbolic_simplifier | 14 | Deep symbolic superoptimization without human knowledge | https://scholar.google.com/scholar?cluster=1299108471437991049&hl=en&as_sdt=0,33 | 4 | 2,020 |
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution | 56 | iclr | 1 | 4 | 2023-06-18 09:10:23.399000 | https://github.com/rl-interpretation/understandingRL | 4 | Explain your move: Understanding agent actions using specific and relevant feature attribution | https://scholar.google.com/scholar?cluster=5830219427979176885&hl=en&as_sdt=0,5 | 0 | 2,020 |
Universal Approximation with Certified Networks | 19 | iclr | 0 | 0 | 2023-06-18 09:10:23.601000 | https://github.com/eth-sri/UniversalCertificationTheory | 10 | Universal approximation with certified networks | https://scholar.google.com/scholar?cluster=8301791316229019028&hl=en&as_sdt=0,21 | 8 | 2,020 |
Measuring and Improving the Use of Graph Information in Graph Neural Networks | 101 | iclr | 10 | 0 | 2023-06-18 09:10:23.806000 | https://github.com/yifan-h/CS-GNN | 77 | Measuring and improving the use of graph information in graph neural networks | https://scholar.google.com/scholar?cluster=6471418699996704565&hl=en&as_sdt=0,10 | 5 | 2,020 |
State-only Imitation with Transition Dynamics Mismatch | 38 | iclr | 3 | 1 | 2023-06-18 09:10:24.010000 | https://github.com/tgangwani/RL-Indirect-imitation | 20 | State-only imitation with transition dynamics mismatch | https://scholar.google.com/scholar?cluster=14672237104350314112&hl=en&as_sdt=0,39 | 4 | 2,020 |
Meta Dropout: Learning to Perturb Latent Features for Generalization | 51 | iclr | 4 | 1 | 2023-06-18 09:10:24.213000 | https://github.com/haebeom-lee/metadrop | 26 | Meta dropout: Learning to perturb latent features for generalization | https://scholar.google.com/scholar?cluster=14333755794039765777&hl=en&as_sdt=0,11 | 3 | 2,020 |
BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget | 25 | iclr | 4 | 1 | 2023-06-18 09:10:24.415000 | https://github.com/BayesWatch/pytorch-blockswap | 32 | Blockswap: Fisher-guided block substitution for network compression on a budget | https://scholar.google.com/scholar?cluster=2671023600912683387&hl=en&as_sdt=0,10 | 8 | 2,020 |
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks | 257 | iclr | 18 | 1 | 2023-06-18 09:10:24.618000 | https://github.com/JHL-HUST/SI-NI-FGSM | 53 | Nesterov accelerated gradient and scale invariance for adversarial attacks | https://scholar.google.com/scholar?cluster=10642064480465270866&hl=en&as_sdt=0,5 | 4 | 2,020 |
Robustness Verification for Transformers | 84 | iclr | 1 | 0 | 2023-06-18 09:10:24.820000 | https://github.com/shizhouxing/Robustness-Verification-for-Transformers | 25 | Robustness verification for transformers | https://scholar.google.com/scholar?cluster=2702221835826609078&hl=en&as_sdt=0,38 | 2 | 2,020 |
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning | 142 | iclr | 9 | 2 | 2023-06-18 09:10:25.024000 | https://github.com/pokaxpoka/netrand | 53 | Network randomization: A simple technique for generalization in deep reinforcement learning | https://scholar.google.com/scholar?cluster=6049043144348184316&hl=en&as_sdt=0,5 | 10 | 2,020 |
Tensor Decompositions for Temporal Knowledge Base Completion | 147 | iclr | 19 | 2 | 2023-06-18 09:10:25.227000 | https://github.com/facebookresearch/tkbc | 65 | Tensor decompositions for temporal knowledge base completion | https://scholar.google.com/scholar?cluster=18234698389055794905&hl=en&as_sdt=0,10 | 9 | 2,020 |
On Universal Equivariant Set Networks | 46 | iclr | 0 | 1 | 2023-06-18 09:10:25.430000 | https://github.com/NimrodSegol/On-Universal-Equivariant-Set-Networks | 10 | On universal equivariant set networks | https://scholar.google.com/scholar?cluster=17434444729278914575&hl=en&as_sdt=0,11 | 1 | 2,020 |
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$ | 3 | iclr | 2 | 0 | 2023-06-18 09:10:25.633000 | https://github.com/fra31/mmr-universal | 6 | Provable robustness against all adversarial -perturbations for | https://scholar.google.com/scholar?cluster=14050453960562252546&hl=en&as_sdt=0,33 | 2 | 2,020 |
Don't Use Large Mini-batches, Use Local SGD | 369 | iclr | 6 | 0 | 2023-06-18 09:10:25.836000 | https://github.com/epfml/LocalSGD-Code | 39 | Don't use large mini-batches, use local sgd | https://scholar.google.com/scholar?cluster=3406394348267726989&hl=en&as_sdt=0,15 | 10 | 2,020 |
Distributionally Robust Neural Networks | 852 | iclr | 39 | 1 | 2023-06-18 09:10:26.040000 | https://github.com/kohpangwei/group_DRO | 184 | Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization | https://scholar.google.com/scholar?cluster=11052704904492332793&hl=en&as_sdt=0,14 | 7 | 2,020 |
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning | 148 | iclr | 15 | 0 | 2023-06-18 09:10:26.243000 | https://github.com/soochan-lee/CN-DPM | 91 | A neural dirichlet process mixture model for task-free continual learning | https://scholar.google.com/scholar?cluster=14278617304843676910&hl=en&as_sdt=0,21 | 7 | 2,020 |