ICLR 2020
825 papers
A Baseline for Few-Shot Image Classification
Guneet S. Dhillon, Pratik Chaudhari, Avinash Ravichandran, Stefano Soatto A Closer Look at Deep Policy Gradients
Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, Simon Lacoste-Julien A Constructive Prediction of the Generalization Error Across Scales
Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, Nir Shavit A Framework for Robustness Certification of Smoothed Classifiers Using F-Divergences
Krishnamurthy Dvijotham, Jamie Hayes, Borja Balle, Zico Kolter, Chongli Qin, Andras Gyorgy, Kai Xiao, Sven Gowal, Pushmeet Kohli A Generalized Training Approach for Multiagent Learning
Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal A Mutual Information Maximization Perspective of Language Representation Learning
Lingpeng Kong, Cyprien de Masson d'Autume, Wang Ling, Lei Yu, Zihang Dai, Dani Yogatama A Probabilistic Formulation of Unsupervised Text Style Transfer
Junxian He, Xinyi Wang, Graham Neubig, Taylor Berg-Kirkpatrick A Stochastic Derivative Free Optimization Method with Momentum
Eduard Gorbunov, Adel Bibi, Ozan Sener, El Houcine Bergou, Peter Richtarik A Theory of Usable Information Under Computational Constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon Abductive Commonsense Reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Scott Wen-tau Yih, Yejin Choi Action Semantics Network: Considering the Effects of Actions in Multiagent Systems
Weixun Wang, Tianpei Yang, Yong Liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Yang Gao Adjustable Real-Time Style Transfer
Mohammad Babaeizadeh, Golnaz Ghiasi Adversarial AutoAugment
Xinyu Zhang, Qiang Wang, Jian Zhang, Zhao Zhong Adversarial Policies: Attacking Deep Reinforcement Learning
Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell Adversarially Robust Representations with Smooth Encoders
Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham, Pushmeet Kohli Adversarially Robust Transfer Learning
Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David Jacobs, Tom Goldstein ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut AMRL: Aggregated Memory for Reinforcement Learning
Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann Analytical Moment Regularizer for Training Robust Networks
Modar Alfadly, Adel Bibi, Muhammed Kocabas, Bernard Ghanem And the Bit Goes Down: Revisiting the Quantization of Neural Networks
Pierre Stock, Armand Joulin, Rémi Gribonval, Benjamin Graham, Hervé Jégou Are Transformers Universal Approximators of Sequence-to-Sequence Functions?
Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar AtomNAS: Fine-Grained End-to-End Neural Architecture Search
Jieru Mei, Yingwei Li, Xiaochen Lian, Xiaojie Jin, Linjie Yang, Alan Yuille, Jianchao Yang Attacking Lifelong Learning Models with Gradient Reversion
Yunhui Guo, Mingrui Liu, Yandong Li, Liqiang Wang, Tianbao Yang, Tajana Rosing AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan Automated Curriculum Generation Through Setter-Solver Interactions
Sebastien Racaniere, Andrew Lampinen, Adam Santoro, David Reichert, Vlad Firoiu, Timothy Lillicrap Automated Relational Meta-Learning
Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li Automatically Discovering and Learning New Visual Categories with Ranking Statistics
Kai Han, Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Andrea Vedaldi, Andrew Zisserman BackPACK: Packing More into Backprop
Felix Dangel, Frederik Kunstner, Philipp Hennig Bayesian Meta Sampling for Fast Uncertainty Adaptation
Zhenyi Wang, Yang Zhao, Ping Yu, Ruiyi Zhang, Changyou Chen BayesOpt Adversarial Attack
Binxin Ru, Adam Cobb, Arno Blaas, Yarin Gal Behaviour Suite for Reinforcement Learning
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt BERTScore: Evaluating Text Generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, Yoav Artzi Branched Multi-Task Networks: Deciding What Layers to Share
Simon Vandenhende, Stamatios Georgoulis, Bert De Brabandere, Luc Van Gool Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin Building Deep Equivariant Capsule Networks
Sai Raam Venkataraman, S. Balasubramanian, R. Raghunatha Sarma Can Gradient Clipping Mitigate Label Noise?
Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar Capsules with Inverted Dot-Product Attention Routing
Yao-Hung Hubert Tsai, Nitish Srivastava, Hanlin Goh, Ruslan Salakhutdinov CAQL: Continuous Action Q-Learning
Moonkyung Ryu, Yinlam Chow, Ross Anderson, Christian Tjandraatmadja, Craig Boutilier Cascade Style Transfer
Zhizhong Wang, Lei Zhao, Qihang Mo, Sihuan Lin, Zhiwen Zuo, Wei Xing, Dongming Lu Certified Defenses for Adversarial Patches
Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studor, Tom Goldstein CLEVRER: CoLlision Events for Video REpresentation and Reasoning
Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, Joshua B. Tenenbaum Collaborative Filtering with a Synthetic Feedback Loop
Wenlin Wang, Hongteng Xu, Ruiyi Zhang, Wenqi Wang, Lawrence Carin Combining Q-Learning and Search with Amortized Value Estimates
Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber, Lars Buesing, Peter W. Battaglia Composing Task-Agnostic Policies with Deep Reinforcement Learning
Ahmed H. Qureshi, Jacob J. Johnson, Yuzhe Qin, Taylor Henderson, Byron Boots, Michael C. Yip Composition-Based Multi-Relational Graph Convolutional Networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Partha Talukdar Compositional Language Continual Learning
Yuanpeng Li, Liang Zhao, Kenneth Church, Mohamed Elhoseiny Compositional Languages Emerge in a Neural Iterated Learning Model
Yi Ren, Shangmin Guo, Matthieu Labeau, Shay B. Cohen, Simon Kirby Compressive Transformers for Long-Range Sequence Modelling
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap Computation Reallocation for Object Detection
Feng Liang, Chen Lin, Ronghao Guo, Ming Sun, Wei Wu, Junjie Yan, Wanli Ouyang Conditional Learning of Fair Representations
Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon Conservative Uncertainty Estimation by Fitting Prior Networks
Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner Continual Learning with Bayesian Neural Networks for Non-Stationary Data
Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, Stephan Günnemann Continual Learning with Hypernetworks
Johannes von Oswald, Christian Henning, João Sacramento, Benjamin F. Grewe Contrastive Representation Distillation
Yonglong Tian, Dilip Krishnan, Phillip Isola Convolutional Conditional Neural Processes
Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner CoPhy: Counterfactual Learning of Physical Dynamics
Fabien Baradel, Natalia Neverova, Julien Mille, Greg Mori, Christian Wolf Critical Initialisation in Continuous Approximations of Binary Neural Networks
George Stamatescu, Federica Gerace, Carlo Lucibello, Ian Fuss, Langford B. White Cross-Dimensional Self-Attention for Multivariate, Geo-Tagged Time Series Imputation
Jiawei Ma, Zheng Shou, Alireza Zareian, Hassan Mansour, Anthony Vetro, Shih-Fu Chang Curvature Graph Network
Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao, Chao Chen Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson Cz-Gem: A Framework for Disentangled Representation Learning
Akash Srivastava, Yamini Bansal, Yukun Ding, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund Data-Dependent Gaussian Prior Objective for Language Generation
Zuchao Li, Rui Wang, Kehai Chen, Masso Utiyama, Eiichiro Sumita, Zhuosheng Zhang, Hai Zhao Data-Independent Neural Pruning via Coresets
Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, Dhruv Batra DDSP: Differentiable Digital Signal Processing
Jesse Engel, Lamtharn Hantrakul, Chenjie Gu, Adam Roberts Decoupling Representation and Classifier for Long-Tailed Recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, Yannis Kalantidis Deep Amortized Clustering
Juho Lee, Yoonho Lee, Yee Whye Teh Deep Audio Priors Emerge from Harmonic Convolutional Networks
Zhoutong Zhang, Yunyun Wang, Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal Deep Double Descent: Where Bigger Models and More Data Hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever Deep Graph Matching Consensus
Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege Deep Neuroethology of a Virtual Rodent
Josh Merel, Diego Aldarondo, Jesse Marshall, Yuval Tassa, Greg Wayne, Bence Ölveczky Deep Orientation Uncertainty Learning Based on a Bingham Loss
Igor Gilitschenski, Roshni Sahoo, Wilko Schwarting, Alexander Amini, Sertac Karaman, Daniela Rus Deep Randomized Least Squares Value Iteration
Guy Adam, Tom Zahavy, Oron Anschel, Nahum Shimkin Deep Reinforcement Learning with Implicit Human Feedback
Duo Xu, Mohit Agarwal, Raghupathy Sivakumar, Faramarz Fekri Deep Semi-Supervised Anomaly Detection
Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft Deep Symbolic Superoptimization Without Human Knowledge
Hui Shi, Yang Zhang, Xinyun Chen, Yuandong Tian, Jishen Zhao DeepSphere: A Graph-Based Spherical CNN
Michaël Defferrard, Martino Milani, Frédérick Gusset, Nathanaël Perraudin DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence Modeling
Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari, Hannaneh Hajishirzi Depth-Adaptive Transformer
Maha Elbayad, Jiatao Gu, Edouard Grave, Michael Auli Depth-Width Trade-Offs for ReLU Networks via Sharkovsky's Theorem
Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas, Xiao Wang Differentiable Architecture Compression
Shashank Singh, Ashish Khetan, Zohar Karnin Differentiable Reasoning over a Virtual Knowledge Base
Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen Differentially Private Meta-Learning
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar Differentiation of Blackbox Combinatorial Solvers
Marin Vlastelica, Anselm Paulus, Vít Musil, Georg Martius, Michal Rolínek DiffTaichi: Differentiable Programming for Physical Simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, Frédo Durand Directional Message Passing for Molecular Graphs
Johannes Klicpera, Janek Groß, Stephan Günnemann Discovering Motor Programs by Recomposing Demonstrations
Tanmay Shankar, Shubham Tulsiani, Lerrel Pinto, Abhinav Gupta Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth
Igor Lovchinsky, Alon Daks, Israel Malkin, Pouya Samangouei, Ardavan Saeedi, Yang Liu, Swami Sankaranarayanan, Tomer Gafner, Ben Sternlieb, Patrick Maher, Nathan Silberman Disentangling Factors of Variations Using Few Labels
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem Disentangling Neural Mechanisms for Perceptual Grouping
Junkyung Kim, Drew Linsley, Kalpit Thakkar, Thomas Serre Distributionally Robust Neural Networks
Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, Percy Liang Domain Adaptive Multibranch Networks
Róger Bermúdez-Chacón, Mathieu Salzmann, Pascal Fua Don't Use Large Mini-Batches, Use Local SGD
Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi Double Neural Counterfactual Regret Minimization
Hui Li, Kailiang Hu, Zhibang Ge, Tao Jiang, Yuan Qi, Le Song Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks
Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin Dream to Control: Learning Behaviors by Latent Imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi Dropout: Explicit Forms and Capacity Control
Raman Arora, Peter Bartlett, Poorya Mianjy, Nathan Srebro DSReg: Using Distant Supervision as a Regularizer
Yuxian Meng, Muyu Li, Xiaoya Li, Wei Wu, Jiwei Li Dynamic Model Pruning with Feedback
Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi Dynamic Time Lag Regression: Predicting What & When
Mandar Chandorkar, Cyril Furtlehner, Bala Poduval, Enrico Camporeale, Michele Sebag Dynamics-Aware Embeddings
William Whitney, Rajat Agarwal, Kyunghyun Cho, Abhinav Gupta Dynamics-Aware Unsupervised Skill Discovery
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman Editable Neural Networks
Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitriy Pyrkin, Sergei Popov, Artem Babenko Efficient Probabilistic Logic Reasoning with Graph Neural Networks
Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song Emergent Tool Use from Multi-Agent Autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
Shell Xu Hu, Pablo G. Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas Damianou Encoding Word Order in Complex Embeddings
Benyou Wang, Donghao Zhao, Christina Lioma, Qiuchi Li, Peng Zhang, Jakob Grue Simonsen Ensemble Distribution Distillation
Andrey Malinin, Bruno Mlodozeniec, Mark Gales Environmental Drivers of Systematicity and Generalization in a Situated Agent
Felix Hill, Andrew Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro Episodic Reinforcement Learning with Associative Memory
Guangxiang Zhu, Zichuan Lin, Guangwen Yang, Chongjie Zhang ES-MAML: Simple Hessian-Free Meta Learning
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang Evaluating the Search Phase of Neural Architecture Search
Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, Mathieu Salzmann Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency
Piyush Gupta, Nikaash Puri, Sukriti Verma, Dhruv Kayastha, Shripad Deshmukh, Balaji Krishnamurthy, Sameer Singh Explanation by Progressive Exaggeration
Sumedha Singla, Brian Pollack, Junxiang Chen, Kayhan Batmanghelich Extreme Tensoring for Low-Memory Preconditioning
Xinyi Chen, Naman Agarwal, Elad Hazan, Cyril Zhang, Yi Zhang Fair Resource Allocation in Federated Learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith Fantastic Generalization Measures and Where to Find Them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio Fast Neural Network Adaptation via Parameter Remapping and Architecture Search
Jiemin Fang, Yuzhu Sun, Kangjian Peng, Qian Zhang, Yuan Li, Wenyu Liu, Xinggang Wang Fast Task Inference with Variational Intrinsic Successor Features
Steven Hansen, Will Dabney, Andre Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih FasterSeg: Searching for Faster Real-Time Semantic Segmentation
Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, Zhangyang Wang Feature Partitioning for Efficient Multi-Task Architectures
Alejandro Newell, Lu Jiang, Chong Wang, Li-Jia Li, Jia Deng Federated Adversarial Domain Adaptation
Xingchao Peng, Zijun Huang, Yizhe Zhu, Kate Saenko Federated Learning with Matched Averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, Yasaman Khazaeni Federated User Representation Learning
Duc Bui, Kshitiz Malik, Jack Goetz, Honglei Liu, Seungwhan Moon, Anuj Kumar, Kang G. Shin FoveaBox: Beyound Anchor-Based Object Detection
Tao Kong, Fuchun Sun, Huaping Liu, Yuning Jiang, Lei Li, Jianbo Shi Frequency-Based Search-Control in Dyna
Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand From Variational to Deterministic Autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, Bernhard Schölkopf Functional Regularisation for Continual Learning with Gaussian Processes
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh Generalization Through Memorization: Nearest Neighbor Language Models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis Generative Models for Effective ML on Private, Decentralized Datasets
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas Generative Ratio Matching Networks
Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton Geom-GCN: Geometric Graph Convolutional Networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang GLAD: Learning Sparse Graph Recovery
Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinvas Aluru, Han Liu, Le Song Global Relational Models of Source Code
Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber Gradient $\ell_1$ Regularization for Quantization Robustness
Milad Alizadeh, Arash Behboodi, Mart van Baalen, Christos Louizos, Tijmen Blankevoort, Max Welling Gradient-Based Neural DAG Learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien Gradientless Descent: High-Dimensional Zeroth-Order Optimization
Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, Qiuyi Zhang Graph Convolutional Reinforcement Learning
Jiechuan Jiang, Chen Dun, Tiejun Huang, Zongqing Lu Graph Inference Learning for Semi-Supervised Classification
Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu GraphAF: A Flow-Based Autoregressive Model for Molecular Graph Generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna Hamiltonian Generative Networks
Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins Harnessing the Power of Infinitely Wide Deep Nets on Small-Data Tasks
Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu High Fidelity Speech Synthesis with Adversarial Networks
Mikołaj Bińkowski, Jeff Donahue, Sander Dieleman, Aidan Clark, Erich Elsen, Norman Casagrande, Luis C. Cobo, Karen Simonyan HighRes-Net: Multi-Frame Super-Resolution by Recursive Fusion
Michel Deudon, Alfredo Kalaitzis, Md Rifat Arefin, Israel Goytom, Zhichao Lin, Kris Sankaran, Vincent Michalski, Samira E Kahou, Julien Cornebise, Yoshua Bengio How Does Learning Rate Decay Help Modern Neural Networks?
Kaichao You, Mingsheng Long, Jianmin Wang, Michael I. Jordan How to 0wn the NAS in Your Spare Time
Sanghyun Hong, Michael Davinroy, Yiǧitcan Kaya, Dana Dachman-Soled, Tudor Dumitraş Hyperbolic Discounting and Learning over Multiple Horizons
William Fedus, Carles Gelada, Yoshua Bengio, Marc G. Bellemare, Hugo Larochelle Hypermodels for Exploration
Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Ian Osband, Zheng Wen, Benjamin Van Roy Image-Guided Neural Object Rendering
Justus Thies, Michael Zollhöfer, Christian Theobalt, Marc Stamminger, Matthias Nießner Implementation Matters in Deep RL: A Case Study on PPO and TRPO
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry Implicit Λ-Jeffreys Autoencoders: Taking the Best of Both Worlds
Aibek Alanov, Max Kochurov, Artem Sobolev, Daniil Yashkov, Dmitry Vetrov Improving End-to-End Object Tracking Using Relational Reasoning
Fabian B. Fuchs, Adam R. Kosiorek, Li Sun, Oiwi Parker Jones, Ingmar Posner Improving Neural Language Generation with Spectrum Control
Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu Incorporating BERT into Neural Machine Translation
Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu Inductive and Unsupervised Representation Learning on Graph Structured Objects
Lichen Wang, Bo Zong, Qianqian Ma, Wei Cheng, Jingchao Ni, Wenchao Yu, Yanchi Liu, Dongjin Song, Haifeng Chen, Yun Fu Inductive Representation Learning on Temporal Graphs
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan Infinite-Horizon Differentiable Model Predictive Control
Sebastian East, Marco Gallieri, Jonathan Masci, Jan Koutnik, Mark Cannon Influence-Based Multi-Agent Exploration
Tonghan Wang, Jianhao Wang, Yi Wu, Chongjie Zhang Insights on Visual Representations for Embodied Navigation Tasks
Erik Wijmans, Julian Straub, Irfan Essa, Dhruv Batra, Judy Hoffman, Ari Morcos Integrative Tensor-Based Anomaly Detection System for Satellites
Youjin Shin, Sangyup Lee, Shahroz Tariq, Myeong Shin Lee, OkchulJung, Daewon Chung, Simon Woo Interpretable Complex-Valued Neural Networks for Privacy Protection
Liyao Xiang, Haotian Ma, Hao Zhang, Yifan Zhang, Jie Ren, Quanshi Zhang Intrinsic Motivation for Encouraging Synergistic Behavior
Rohan Chitnis, Shubham Tulsiani, Saurabh Gupta, Abhinav Gupta ISBNet: Instance-Aware Selective Branching Networks
Shaofeng Cai, Yao Shu, Wei Wang, Gang Chen, Beng Chin Ooi Jelly Bean World: A Testbed for Never-Ending Learning
Emmanouil Antonios Platanios, Abulhair Saparov, Tom Mitchell Kaleidoscope: An Efficient, Learnable Representation for All Structured Linear Maps
Tri Dao, Nimit Sohoni, Albert Gu, Matthew Eichhorn, Amit Blonder, Megan Leszczynski, Atri Rudra, Christopher Ré Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning
Noah Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin Riedmiller Kernelized Wasserstein Natural Gradient
Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montufar Knowledge Consistency Between Neural Networks and Beyond
Ruofan Liang, Tianlin Li, Longfei Li, Jing Wang, Quanshi Zhang Kronecker Attention Networks
Hongyang Gao, Zhengyang Wang, Shuiwang Ji Lagrangian Fluid Simulation with Continuous Convolutions
Benjamin Ummenhofer, Lukas Prantl, Nils Thuerey, Vladlen Koltun Language GANs Falling Short
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin Large Batch Optimization for Deep Learning: Training BERT in 76 Minutes
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh LDMGAN: Reducing Mode Collapse in GANs with Latent Distribution Matching
Zhiwen Zuo, Lei Zhao, Huiming Zhang, Qihang Mo, Haibo Chen, Zhizhong Wang, AiLin Li, Lihong Qiu, Wei Xing, Dongming Lu Learned Step Size Quantization
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, Dharmendra S. Modha Learning Execution Through Neural Code Fusion
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi Learning Expensive Coordination: An Event-Based Deep RL Approach
Zhenyu Shi, Runsheng Yu, Xinrun Wang, Rundong Wang, Youzhi Zhang, Hanjiang Lai, Bo An Learning Explainable Models Using Attribution Priors
Gabriel Erion, Joseph D. Janizek, Pascal Sturmfels, Scott Lundberg, Su-In Lee Learning from Explanations with Neural Execution Tree
Ziqi Wang, Yujia Qin, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Leonardo Neves, Zhiyuan Liu, Xiang Ren Learning from Rules Generalizing Labeled Exemplars
Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, Sunita Sarawagi Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping
Adam W. Harley, Shrinidhi K. Lakshmikanth, Fangyu Li, Xian Zhou, Hsiao-Yu Fish Tung, Katerina Fragkiadaki Learning Robust Representations via Multi-View Information Bottleneck
Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, Zeynep Akata Learning Space Partitions for Nearest Neighbor Search
Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner Learning the Arrow of Time for Problems in Reinforcement Learning
Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-Distribution Tasks
Hae Beom Lee, Hayeon Lee, Donghyun Na, Saehoon Kim, Minseop Park, Eunho Yang, Sung Ju Hwang Learning to Explore Using Active Neural SLAM
Devendra Singh Chaplot, Dhiraj Gandhi, Saurabh Gupta, Abhinav Gupta, Ruslan Salakhutdinov Learning to Learn by Zeroth-Order Oracle
Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh Learning to Link
Maria-Florina Balcan, Travis Dick, Manuel Lang Learning to Move with Affordance Maps
William Qi, Ravi Teja Mullapudi, Saurabh Gupta, Deva Ramanan Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, Caiming Xiong Learning to Solve the Credit Assignment Problem
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