ICLR 2019
502 papers
A Closer Look at Few-Shot Classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, Jia-Bin Huang A Generative Model for Electron Paths
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato A Kernel Random Matrix-Based Approach for Sparse PCA
Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet A Mean Field Theory of Batch Normalization
Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S. Schoenholz A Rotation-Equivariant Convolutional Neural Network Model of Primary Visual Cortex
Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis, Paul G. Fahey, Santiago A. Cadena, Edgar Y. Walker, Erick Cobos, Jacob Reimer, Andreas S. Tolias, Matthias Bethge A Universal Music Translation Network
Noam Mor, Lior Wolf, Adam Polyak, Yaniv Taigman A Variational Inequality Perspective on Generative Adversarial Networks
Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, Simon Lacoste-Julien Accumulation Bit-Width Scaling for Ultra-Low Precision Training of Deep Networks
Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh Shanbhag, Kailash Gopalakrishnan Active Learning with Partial Feedback
Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods
Zhiming Zhou, Qingru Zhang, Guansong Lu, Hongwei Wang, Weinan Zhang, Yong Yu Adversarial Audio Synthesis
Chris Donahue, Julian McAuley, Miller Puckette Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
Ali Farshchian, Juan A. Gallego, Joseph P. Cohen, Yoshua Bengio, Lee E. Miller, Sara A. Solla Adversarial Reprogramming of Neural Networks
Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein Aggregated Momentum: Stability Through Passive Damping
James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse An Empirical Study of Binary Neural Networks' Optimisation
Milad Alizadeh, Javier Fernández-Marqués, Nicholas D. Lane, Yarin Gal An Empirical Study of Example Forgetting During Deep Neural Network Learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, Geoffrey J. Gordon Analysing Mathematical Reasoning Abilities of Neural Models
David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli Analysis of Quantized Models
Lu Hou, Ruiliang Zhang, James T. Kwok Analyzing Inverse Problems with Invertible Neural Networks
Lynton Ardizzone, Jakob Kruse, Carsten Rother, Ullrich Köthe Are Adversarial Examples Inevitable?
Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein Attentive Neural Processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh AutoLoss: Learning Discrete Schedule for Alternate Optimization
Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio Bayesian Deep Convolutional Networks with Many Channels Are Gaussian Processes
Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-dickstein Bayesian Policy Optimization for Model Uncertainty
Gilwoo Lee, Brian Hou, Aditya Mandalika, Jeongseok Lee, Sanjiban Choudhury, Siddhartha S. Srinivasa Beyond Greedy Ranking: Slate Optimization via List-CVAE
Ray Jiang, Sven Gowal, Yuqiu Qian, Timothy Mann, Danilo J. Rezende Boosting Robustness Certification of Neural Networks
Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces
Senthil Purushwalkam, Abhinav Gupta, Danny Kaufman, Bryan Russell Building Dynamic Knowledge Graphs from Text Using Machine Reading Comprehension
Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan, Adam Trischler, Andrew McCallum Competitive Experience Replay
Hao Liu, Alexander Trott, Richard Socher, Caiming Xiong Complement Objective Training
Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan Composing Complex Skills by Learning Transition Policies
Youngwoon Lee, Shao-Hua Sun, Sriram Somasundaram, Edward S. Hu, Joseph J. Lim Conditional Network Embeddings
Bo Kang, Jefrey Lijffijt, Tijl De Bie Contingency-Aware Exploration in Reinforcement Learning
Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, Honglak Lee Critical Learning Periods in Deep Networks
Alessandro Achille, Matteo Rovere, Stefano Soatto DARTS: Differentiable Architecture Search
Hanxiao Liu, Karen Simonyan, Yiming Yang Deep Anomaly Detection with Outlier Exposure
Dan Hendrycks, Mantas Mazeika, Thomas Dietterich Deep Convolutional Networks as Shallow Gaussian Processes
Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison Deep Frank-Wolfe for Neural Network Optimization
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar Deep Graph Infomax
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm Deep Layers as Stochastic Solvers
Adel Bibi, Bernard Ghanem, Vladlen Koltun, Rene Ranftl Deep Reinforcement Learning with Relational Inductive Biases
Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, Peter Battaglia Deterministic Variational Inference for Robust Bayesian Neural Networks
Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt DHER: Hindsight Experience Replay for Dynamic Goals
Meng Fang, Cheng Zhou, Bei Shi, Boqing Gong, Jia Xu, Tong Zhang Diffusion Scattering Transforms on Graphs
Fernando Gama, Alejandro Ribeiro, Joan Bruna Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
Marc T Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S Zemel Discriminator Rejection Sampling
Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian Goodfellow, Augustus Odena Disjoint Mapping Network for Cross-Modal Matching of Voices and Faces
Yandong Wen, Mahmoud Al Ismail, Weiyang Liu, Bhiksha Raj, Rita Singh Diversity-Sensitive Conditional Generative Adversarial Networks
Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, Honglak Lee Do Deep Generative Models Know What They Don't Know? Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Francesco Moramarco, Jack Flann, Nils Y. Hammerla DPSNet: End-to-End Deep Plane Sweep Stereo
Sunghoon Im, Hae-Gon Jeon, Stephen Lin, In So Kweon Dynamic Channel Pruning: Feature Boosting and Suppression
Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, Cheng-zhong Xu Dynamic Sparse Graph for Efficient Deep Learning
Liu Liu, Lei Deng, Xing Hu, Maohua Zhu, Guoqi Li, Yufei Ding, Yuan Xie DyRep: Learning Representations over Dynamic Graphs
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha Efficient Lifelong Learning with A-GEM
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, Mohamed Elhoseiny Efficient Training on Very Large Corpora via Gramian Estimation
Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed Chi, John Anderson Eidetic 3D LSTM: A Model for Video Prediction and Beyond
Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei Emergent Coordination Through Competition
Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse Engel, Douglas Eck Environment Probing Interaction Policies
Wenxuan Zhou, Lerrel Pinto, Abhinav Gupta Episodic Curiosity Through Reachability
Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphael Marinier, Marc Pollefeys, Timothy Lillicrap, Sylvain Gelly Equi-Normalization of Neural Networks
Pierre Stock, Benjamin Graham, Rémi Gribonval, Hervé Jégou Excessive Invariance Causes Adversarial Vulnerability
Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge Explaining Image Classifiers by Counterfactual Generation
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud Exploration by Random Network Distillation
Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov Feature Intertwiner for Object Detection
Hongyang Li, Bo Dai, Shaoshuai Shi, Wanli Ouyang, Xiaogang Wang Feature-Wise Bias Amplification
Klas Leino, Emily Black, Matt Fredrikson, Shayak Sen, Anupam Datta FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud Functional Variational Bayesian Neural Networks
Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger Grosse G-SGD: Optimizing ReLU Neural Networks in Its Positively Scale-Invariant Space
Qi Meng, Shuxin Zheng, Huishuai Zhang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Nenghai Yu, Tie-Yan Liu GamePad: A Learning Environment for Theorem Proving
Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba GANSynth: Adversarial Neural Audio Synthesis
Jesse Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts Generative Code Modeling with Graphs
Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov GO Gradient for Expectation-Based Objectives
Yulai Cong, Miaoyun Zhao, Ke Bai, Lawrence Carin Graph Wavelet Neural Network
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng Guiding Policies with Language via Meta-Learning
John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, Jacob Andreas, John DeNero, Pieter Abbeel, Sergey Levine H-Detach: Modifying the LSTM Gradient Towards Better Optimization
Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio Hierarchical Generative Modeling for Controllable Speech Synthesis
Wei-Ning Hsu, Yu Zhang, Ron J. Weiss, Heiga Zen, Yonghui Wu, Yuxuan Wang, Yuan Cao, Ye Jia, Zhifeng Chen, Jonathan Shen, Patrick Nguyen, Ruoming Pang Hierarchical Visuomotor Control of Humanoids
Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Greg Wayne Hindsight Policy Gradients
Paulo Rauber, Avinash Ummadisingu, Filipe Mutz, Jürgen Schmidhuber How Important Is a Neuron
Kedar Dhamdhere, Mukund Sundararajan, Qiqi Yan How Powerful Are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka How to Train Your MAML
Antreas Antoniou, Harrison Edwards, Amos Storkey Human-Level Protein Localization with Convolutional Neural Networks
Elisabeth Rumetshofer, Markus Hofmarcher, Clemens Röhrl, Sepp Hochreiter, Günter Klambauer Hyperbolic Attention Networks
Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas Identifying and Controlling Important Neurons in Neural Machine Translation
Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, James Glass ImageNet-Trained CNNs Are Biased Towards Texture; Increasing Shape Bias Improves Accuracy and Robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel Imposing Category Trees onto Word-Embeddings Using a Geometric Construction
Tiansi Dong, Chrisitan Bauckhage, Hailong Jin, Juanzi Li, Olaf Cremers, Daniel Speicher, Armin B. Cremers, Joerg Zimmermann Improving Sequence-to-Sequence Learning via Optimal Transport
Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan, Haichao Zhang, Bai Li, Dinghan Shen, Changyou Chen, Lawrence Carin InfoBot: Transfer and Exploration via the Information Bottleneck
Anirudh Goyal, Riashat Islam, Dj Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Yoshua Bengio, Sergey Levine Information Asymmetry in KL-Regularized RL
Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess Information-Directed Exploration for Deep Reinforcement Learning
Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp, Andreas Krause Invariant and Equivariant Graph Networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman Kernel RNN Learning (KeRNL)
Christopher Roth, Ingmar Kanitscheider, Ila Fiete Knowledge Flow: Improve upon Your Teachers
Iou-Jen Liu, Jian Peng, Alexander Schwing Label Super-Resolution Networks
Kolya Malkin, Caleb Robinson, Le Hou, Rachel Soobitsky, Jacob Czawlytko, Dimitris Samaras, Joel Saltz, Lucas Joppa, Nebojsa Jojic LanczosNet: Multi-Scale Deep Graph Convolutional Networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel Large-Scale Study of Curiosity-Driven Learning
Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, Alexei A. Efros Latent Convolutional Models
ShahRukh Athar, Evgeny Burnaev, Victor Lempitsky Learning a SAT Solver from Single-Bit Supervision
Daniel Selsam, Matthew Lamm, Benedikt B\"{u}nz, Percy Liang, Leonardo de Moura, David L. Dill Learning Concise Representations for Regression by Evolving Networks of Trees
William La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore Learning Deep Representations by Mutual Information Estimation and Maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, Yoshua Bengio Learning Embeddings into Entropic Wasserstein Spaces
Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon Learning Factorized Multimodal Representations
Yao-Hung Hubert Tsai, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency, Ruslan Salakhutdinov Learning Factorized Representations for Open-Set Domain Adaptation
Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann Learning Multi-Level Hierarchies with Hindsight
Andrew Levy, George Konidaris, Robert Platt, Kate Saenko Learning Neural PDE Solvers with Convergence Guarantees
Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon Learning Protein Structure with a Differentiable Simulator
John Ingraham, Adam Riesselman, Chris Sander, Debora Marks Learning Recurrent Binary/Ternary Weights
Arash Ardakani, Zhengyun Ji, Sean C. Smithson, Brett H. Meyer, Warren J. Gross Learning Sparse Relational Transition Models
Victoria Xia, Zi Wang, Kelsey Allen, Tom Silver, Leslie Pack Kaelbling Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn Learning to Describe Scenes with Programs
Yunchao Liu, Zheng Wu, Daniel Ritchie, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu Learning to Design RNA
Frederic Runge, Danny Stoll, Stefan Falkner, Frank Hutter Learning to Infer and Execute 3D Shape Programs
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu Learning to Learn with Conditional Class Dependencies
Xiang Jiang, Mohammad Havaei, Farshid Varno, Gabriel Chartrand, Nicolas Chapados, Stan Matwin Learning to Learn Without Forgetting by Maximizing Transfer and Minimizing Interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro Learning to Make Analogies by Contrasting Abstract Relational Structure
Felix Hill, Adam Santoro, David Barrett, Ari Morcos, Timothy Lillicrap Learning to Navigate the Web
Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust, Dilek Hakkani-Tur Learning to Propagate Labels: Transductive Propagation Network for Few-Shot Learning
Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang Learning to Represent Edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt Learning to Schedule Communication in Multi-Agent Reinforcement Learning
Daewoo Kim, Sangwoo Moon, David Hostallero, Wan Ju Kang, Taeyoung Lee, Kyunghwan Son, Yung Yi Learning to Simulate
Nataniel Ruiz, Samuel Schulter, Manmohan Chandraker Learning to Understand Goal Specifications by Modelling Reward
Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Arian Hosseini, Pushmeet Kohli, Edward Grefenstette Learning What and Where to Attend
Drew Linsley, Dan Shiebler, Sven Eberhardt, Thomas Serre Learning What You Can Do Before Doing Anything
Oleh Rybkin, Karl Pertsch, Konstantinos G. Derpanis, Kostas Daniilidis, Andrew Jaegle Learning-Based Frequency Estimation Algorithms
Chen-Yu Hsu, Piotr Indyk, Dina Katabi, Ali Vakilian LeMoNADe: Learned Motif and Neuronal Assembly Detection in Calcium Imaging Videos
Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A Hamprecht Meta-Learning for Stochastic Gradient MCMC
Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato Meta-Learning Probabilistic Inference for Prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard Turner Meta-Learning Update Rules for Unsupervised Representation Learning
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein Meta-Learning with Differentiable Closed-Form Solvers
Luca Bertinetto, Joao F. Henriques, Philip Torr, Andrea Vedaldi Meta-Learning with Latent Embedding Optimization
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell Mode Normalization
Lucas Deecke, Iain Murray, Hakan Bilen Modeling the Long Term Future in Model-Based Reinforcement Learning
Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati, Anirudh Goyal, Yoshua Bengio, Devi Parikh, Dhruv Batra Modeling Uncertainty with Hedged Instance Embeddings
Seong Joon Oh, Kevin P. Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, Andrew C. Gallagher Multi-Agent Dual Learning
Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, Tie-Yan Liu Multi-Class Classification Without Multi-Class Labels
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira Multi-Domain Adversarial Learning
Alice Schoenauer-Sebag, Louise Heinrich, Marc Schoenauer, Michele Sebag, Lani F. Wu, Steve J. Altschuler Multiple-Attribute Text Rewriting
Guillaume Lample, Sandeep Subramanian, Eric Smith, Ludovic Denoyer, Marc'Aurelio Ranzato, Y-Lan Boureau Music Transformer: Generating Music with Long-Term Structure
Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Curtis Hawthorne, Noam Shazeer, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck Neural Logic Machines
Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Denny Zhou Neural Network Gradient-Based Learning of Black-Box Function Interfaces
Alon Jacovi, Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, Jonathan Berant Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, Karsten Borgwardt Neural Probabilistic Motor Primitives for Humanoid Control
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess Neural Program Repair by Jointly Learning to Localize and Repair
Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh Neural Speed Reading with Structural-Jump-LSTM
Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey On the Sensitivity of Adversarial Robustness to Input Data Distributions
Gavin Weiguang Ding, Kry Yik Chau Lui, Xiaomeng Jin, Luyu Wang, Ruitong Huang Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
Mohammad Kachuee, Orpaz Goldstein, Kimmo Kärkkäinen, Sajad Darabi, Majid Sarrafzadeh Optimistic Mirror Descent in Saddle-Point Problems: Going the Extra (gradient) Mile
Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras Overcoming Catastrophic Forgetting for Continual Learning via Model Adaptation
Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Zhengwei Tao, Jinwen Ma, Dongyan Zhao, Rui Yan Pay Less Attention with Lightweight and Dynamic Convolutions
Felix Wu, Angela Fan, Alexei Baevski, Yann Dauphin, Michael Auli PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Jan Svoboda, Jonathan Masci, Federico Monti, Michael Bronstein, Leonidas Guibas Phase-Aware Speech Enhancement with Deep Complex U-Net
Hyeong-Seok Choi, Jang-Hyun Kim, Jaesung Huh, Adrian Kim, Jung-Woo Ha, Kyogu Lee Poincare Glove: Hyperbolic Word Embeddings
Alexandru Tifrea, Gary Becigneul, Octavian-Eugen Ganea Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu Preferences Implicit in the State of the World
Rohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, Anca Dragan Preventing Posterior Collapse with Delta-VAEs
Ali Razavi, Aaron van den Oord, Ben Poole, Oriol Vinyals Probabilistic Planning with Sequential Monte Carlo Methods
Alexandre Piche, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, Chris Pal ProMP: Proximal Meta-Policy Search
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel Quaternion Recurrent Neural Networks
Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Chiheb Trabelsi, Renato De Mori, Yoshua Bengio Query-Efficient Hard-Label Black-Box Attack: An Optimization-Based Approach
Minhao Cheng, Thong Le, Pin-Yu Chen, Huan Zhang, JinFeng Yi, Cho-Jui Hsieh Random Mesh Projectors for Inverse Problems
Konik Kothari, Sidharth Gupta, Maarten v. de Hoop, Ivan Dokmanic Reasoning About Physical Interactions with Object-Oriented Prediction and Planning
Michael Janner, Sergey Levine, William T. Freeman, Joshua B. Tenenbaum, Chelsea Finn, Jiajun Wu Recall Traces: Backtracking Models for Efficient Reinforcement Learning
Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio Recurrent Experience Replay in Distributed Reinforcement Learning
Steven Kapturowski, Georg Ostrovski, John Quan, Remi Munos, Will Dabney Regularized Learning for Domain Adaptation Under Label Shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar Relational Forward Models for Multi-Agent Learning
Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinicius Zambaldi, János Kramár, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia Relaxed Quantization for Discretized Neural Networks
Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, Max Welling Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Richard G. Baraniuk, Swarat Chaudhuri, Ankit B. Patel Rethinking the Value of Network Pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, Trevor Darrell Revealing Interpretable Object Representations from Human Behavior
Charles Y. Zheng, Francisco Pereira, Chris I. Baker, Martin N. Hebart Reward Constrained Policy Optimization
Chen Tessler, Daniel J. Mankowitz, Shie Mannor Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
Jonathan Uesato, Ananya Kumar, Csaba Szepesvari, Tom Erez, Avraham Ruderman, Keith Anderson, Krishnamurthy Dvijotham, Nicolas Heess, Pushmeet Kohli RNNs Implicitly Implement Tensor-Product Representations
R. Thomas McCoy, Tal Linzen, Ewan Dunbar, Paul Smolensky Robust Conditional Generative Adversarial Networks
Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou Robustness May Be at Odds with Accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry Sample Efficient Adaptive Text-to-Speech
Yutian Chen, Yannis Assael, Brendan Shillingford, David Budden, Scott Reed, Heiga Zen, Quan Wang, Luis C. Cobo, Andrew Trask, Ben Laurie, Caglar Gulcehre, Aäron van den Oord, Oriol Vinyals, Nando de Freitas Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
Chih-Yao Ma, Jiasen Lu, Zuxuan Wu, Ghassan AlRegib, Zsolt Kira, Richard Socher, Caiming Xiong Selfless Sequential Learning
Rahaf Aljundi, Marcus Rohrbach, Tinne Tuytelaars signSGD via Zeroth-Order Oracle
Sijia Liu, Pin-Yu Chen, Xiangyi Chen, Mingyi Hong signSGD with Majority Vote Is Communication Efficient and Fault Tolerant
Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar Sliced Wasserstein Auto-Encoders
Soheil Kolouri, Phillip E. Pope, Charles E. Martin, Gustavo K. Rohde Slimmable Neural Networks
Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, Thomas Huang Smoothing the Geometry of Probabilistic Box Embeddings
Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum SNAS: Stochastic Neural Architecture Search
Sirui Xie, Hehui Zheng, Chunxiao Liu, Liang Lin Solving the Rubik's Cube with Approximate Policy Iteration
Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi SOM-VAE: Interpretable Discrete Representation Learning on Time Series
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch Spectral Inference Networks: Unifying Deep and Spectral Learning
David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld Spherical CNNs on Unstructured Grids
Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Niessner Spreading Vectors for Similarity Search
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Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola Transfer Learning for Sequences via Learning to Collocate
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Shaojie Bai, J. Zico Kolter, Vladlen Koltun Universal Successor Features Approximators
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Arthur Pajot, Emmanuel de Bezenac, Patrick Gallinari Unsupervised Control Through Non-Parametric Discriminative Rewards
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, Volodymyr Mnih Unsupervised Discovery of Parts, Structure, and Dynamics
Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu Value Propagation Networks
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Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh Verification of Non-Linear Specifications for Neural Networks
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Wei Yang, Xiaolong Wang, Ali Farhadi, Abhinav Gupta, Roozbeh Mottaghi Wasserstein Barycenter Model Ensembling
Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero Dos Santos, Tom Sercu What Do You Learn from Context? Probing for Sentence Structure in Contextualized Word Representations
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