ICLR 2017
309 papers
A Compositional Object-Based Approach to Learning Physical Dynamics
Michael Chang, Tomer D. Ullman, Antonio Torralba, Joshua B. Tenenbaum A Differentiable Physics Engine for Deep Learning in Robotics
Jonas Degrave, Michiel Hermans, Joni Dambre, Francis Wyffels A Learned Representation for Artistic Style
Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur A Structured Self-Attentive Sentence Embedding
Zhouhan Lin, Minwei Feng, Cícero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio Accelerating Eulerian Fluid Simulation with Convolutional Networks
Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin Adversarial Attacks on Neural Network Policies
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel Adversarial Examples for Semantic Image Segmentation
Volker Fischer, Mummadi Chaithanya Kumar, Jan Hendrik Metzen, Thomas Brox Adversarial Examples in the Physical World
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio Adversarial Feature Learning
Jeff Donahue, Philipp Krähenbühl, Trevor Darrell Adversarial Machine Learning at Scale
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio Adversarially Learned Inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, Aaron C. Courville Amortised MAP Inference for Image Super-Resolution
Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár An Actor-Critic Algorithm for Sequence Prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. Courville, Yoshua Bengio Audio Super-Resolution Using Neural Networks
Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon Batch Policy Gradient Methods for Improving Neural Conversation Models
Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter Beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, Alexander Lerchner Bidirectional Attention Flow for Machine Comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi Bit-Pragmatic Deep Neural Network Computing
Jorge Albericio, Patrick Judd, Alberto Delmas, Sayeh Sharify, Andreas Moshovos Calibrating Energy-Based Generative Adversarial Networks
Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, Aaron C. Courville Capacity and Trainability in Recurrent Neural Networks
Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, Susanne Saminger-Platz Char2Wav: End-to-End Speech Synthesis
Jose Sotelo, Soroush Mehri, Kundan Kumar, João Felipe Santos, Kyle Kastner, Aaron C. Courville, Yoshua Bengio Combining Policy Gradient and Q-Learning
Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih CommAI: Evaluating the First Steps Towards a Useful General AI
Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomás Mikolov Compositional Kernel Machines
Robert Gens, Pedro M. Domingos Dance Dance Convolution
Chris Donahue, Zachary C. Lipton, Julian J. McAuley Data Noising as Smoothing in Neural Network Language Models
Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, Andrew Y. Ng Deep Information Propagation
Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein Deep Learning with Dynamic Computation Graphs
Moshe Looks, Marcello Herreshoff, DeLesley Hutchins, Peter Norvig Deep Learning with Sets and Point Clouds
Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos Deep Nets Don't Learn via Memorization
David Krueger, Nicolas Ballas, Stanislaw Jastrzebski, Devansh Arpit, Maxinder S. Kanwal, Tegan Maharaj, Emmanuel Bengio, Asja Fischer, Aaron C. Courville Deep Probabilistic Programming
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei Deep Variational Information Bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy DeepCoder: Learning to Write Programs
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow Density Estimation Using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio Dialogue Learning with Human-in-the-Loop
Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston Diet Networks: Thin Parameters for Fat Genomics
Adriana Romero, Pierre Luc Carrier, Akram Erraqabi, Tristan Sylvain, Alex Auvolat, Etienne Dejoie, Marc-André Legault, Marie-Pierre Dubé, Julie G. Hussin, Yoshua Bengio Discovering Objects and Their Relations from Entangled Scene Representations
David Raposo, Adam Santoro, David G. T. Barrett, Razvan Pascanu, Tim Lillicrap, Peter W. Battaglia Do Deep Convolutional Nets Really Need to Be Deep and Convolutional?
Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson, Rich Caruana Dropout with Expectation-Linear Regularization
Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard H. Hovy DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Enhao Gong, Shijian Tang, Erich Elsen, Peter Vajda, Manohar Paluri, John Tran, Bryan Catanzaro, William J. Dally Embracing Data Abundance
Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst End-to-End Optimized Image Compression
Johannes Ballé, Valero Laparra, Eero P. Simoncelli Energy-Based Generative Adversarial Networks
Junbo Jake Zhao, Michaël Mathieu, Yann LeCun Entropy-SGD: Biasing Gradient Descent into Wide Valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine Explaining the Learning Dynamics of Direct Feedback Alignment
Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein Exploring Sparsity in Recurrent Neural Networks
Sharan Narang, Greg Diamos, Shubho Sengupta, Erich Elsen Exponential Machines
Alexander Novikov, Mikhail Trofimov, Ivan V. Oseledets Extrapolation and Learning Equations
Georg Martius, Christoph H. Lampert Fast Generation for Convolutional Autoregressive Models
Prajit Ramachandran, Tom Le Paine, Pooya Khorrami, Mohammad Babaeizadeh, Shiyu Chang, Yang Zhang, Mark A. Hasegawa-Johnson, Roy H. Campbell, Thomas S. Huang Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Jongsoo Park, Sheng R. Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, Pradeep Dubey Filter Shaping for Convolutional Neural Networks
Xingyi Li, Fuxin Li, Xiaoli Z. Fern, Raviv Raich Frustratingly Short Attention Spans in Neural Language Modeling
Michal Daniluk, Tim Rocktäschel, Johannes Welbl, Sebastian Riedel Gated Multimodal Units for Information Fusion
John Edison Arevalo Ovalle, Thamar Solorio, Manuel Montes-y-Gómez, Fabio A. González Generalizing Skills with Semi-Supervised Reinforcement Learning
Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton Generative Multi-Adversarial Networks
Ishan P. Durugkar, Ian Gemp, Sridhar Mahadevan Geometry of Polysemy
Jiaqi Mu, Suma Bhat, Pramod Viswanath Hadamard Product for Low-Rank Bilinear Pooling
Jin-Hwa Kim, Kyoung Woon On, Woosang Lim, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization
Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar HyperNetworks
David Ha, Andrew M. Dai, Quoc V. Le Joint Embeddings of Scene Graphs and Images
Eugene Belilovsky, Matthew B. Blaschko, Jamie Ryan Kiros, Raquel Urtasun, Richard S. Zemel Latent Sequence Decompositions
William Chan, Yu Zhang, Quoc V. Le, Navdeep Jaitly Learning a Natural Language Interface with Neural Programmer
Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, Dario Amodei Learning Algorithms for Active Learning
Philip Bachman, Alessandro Sordoni, Adam Trischler Learning Curve Prediction with Bayesian Neural Networks
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter Learning End-to-End Goal-Oriented Dialog
Antoine Bordes, Y-Lan Boureau, Jason Weston Learning Features of Music from Scratch
John Thickstun, Zaïd Harchaoui, Sham M. Kakade Learning Through Dialogue Interactions by Asking Questions
Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston Learning to Compose Words into Sentences with Reinforcement Learning
Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling Learning to Discover Sparse Graphical Models
Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew B. Blaschko Learning to Navigate in Complex Environments
Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andy Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, Dharshan Kumaran, Raia Hadsell Learning to Optimize
Ke Li, Jitendra Malik Learning to Perform Physics Experiments via Deep Reinforcement Learning
Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter W. Battaglia, Nando de Freitas Learning to Query, Reason, and Answer Questions on Ambiguous Texts
Xiaoxiao Guo, Tim Klinger, Clemens Rosenbaum, Joseph P. Bigus, Murray Campbell, Ban Kawas, Kartik Talamadupula, Gerry Tesauro, Satinder Singh Learning to Remember Rare Events
Lukasz Kaiser, Ofir Nachum, Aurko Roy, Samy Bengio Learning to Superoptimize Programs
Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli Lifelong Perceptual Programming by Example
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow Lossy Image Compression with Compressive Autoencoders
Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár Maximum Entropy Flow Networks
Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham Metacontrol for Adaptive Imagination-Based Optimization
Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia Mode Regularized Generative Adversarial Networks
Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li Mollifying Networks
Çaglar Gülçehre, Marcin Moczulski, Francesco Visin, Yoshua Bengio Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses
Eleanor Batty, Josh Merel, Nora Brackbill, Alexander Heitman, Alexander Sher, Alan M. Litke, E. J. Chichilnisky, Liam Paninski Multiplicative LSTM for Sequence Modelling
Ben Krause, Iain Murray, Steve Renals, Liang Lu Neu0
Karthik R, Aman Achpal, Vinayshekhar Bk, Anantharaman Palacode Narayana Iyer, Channa Bankapur Neural Combinatorial Optimization with Reinforcement Learning
Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, Samy Bengio Neural Expectation Maximization
Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber Neural Functional Programming
John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow Neural Program Lattices
Chengtao Li, Daniel Tarlow, Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman Neuro-Symbolic Program Synthesis
Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli Nonparametric Neural Networks
George Philipp, Jaime G. Carbonell On Detecting Adversarial Perturbations
Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff On Hyperparameter Optimization in Learning Systems
Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil On Improving the Numerical Stability of Winograd Convolutions
Kevin Vincent, Kevin Stephano, Michael A. Frumkin, Boris Ginsburg, Julien Demouth On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang Online Bayesian Transfer Learning for Sequential Data Modeling
Priyank Jaini, Zhitang Chen, Pablo Carbajal, Edith Law, Laura Middleton, Kayla Regan, Mike Schaekermann, George Trimponias, James Tung, Pascal Poupart Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, Jeff Dean Particle Value Functions
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh PixelVAE: A Latent Variable Model for Natural Images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taïga, Francesco Visin, David Vázquez, Aaron C. Courville Pl@ntNet App in the Era of Deep Learning
Antoine Affouard, Hervé Goëau, Pierre Bonnet, Jean-Christophe Lombardo, Alexis Joly Pointer Sentinel Mixture Models
Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher Programming with a Differentiable Forth Interpreter
Matko Bosnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel Pruning Filters for Efficient ConvNets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, Hans Peter Graf Q-Prop: Sample-Efficient Policy Gradient with an Off-Policy Critic
Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine Quasi-Recurrent Neural Networks
James Bradbury, Stephen Merity, Caiming Xiong, Richard Socher Query-Reduction Networks for Question Answering
Min Joon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi Recurrent Batch Normalization
Tim Cooijmans, Nicolas Ballas, César Laurent, Çaglar Gülçehre, Aaron C. Courville Recurrent Environment Simulators
Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed Recurrent Hidden Semi-Markov Model
Hanjun Dai, Bo Dai, Yan-Ming Zhang, Shuang Li, Le Song Recurrent Normalization Propagation
César Laurent, Nicolas Ballas, Pascal Vincent Regularizing CNNs with Locally Constrained Decorrelations
Pau Rodríguez, Jordi Gonzàlez, Guillem Cucurull, Josep M. Gonfaus, F. Xavier Roca Regularizing Neural Networks by Penalizing Confident Output Distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton Reinforcement Learning Through Asynchronous Advantage Actor-Critic on a GPU
Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz Reinforcement Learning with Unsupervised Auxiliary Tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu Revisiting Batch Normalization for Practical Domain Adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, Xiaodi Hou Sample Efficient Actor-Critic with Experience Replay
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron C. Courville, Yoshua Bengio Semantic Embeddings for Program Behaviour Patterns
Alexander Chistyakov, Ekaterina Lobacheva, Arseny Kuznetsov, Alexey Romanenko Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar Short and Deep: Sketching and Neural Networks
Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar Snapshot Ensembles: Train 1, Get M for Free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger Steerable CNNs
Taco S. Cohen, Max Welling Structured Attention Networks
Yoon Kim, Carl Denton, Luong Hoang, Alexander M. Rush Support Regularized Sparse Coding and Its Fast Encoder
Yingzhen Yang, Jiahui Yu, Pushmeet Kohli, Jianchao Yang, Thomas S. Huang Synthetic Gradient Methods with Virtual Forward-Backward Networks
Takeru Miyato, Daisuke Okanohara, Shin-ichi Maeda, Masanori Koyama Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun The Effectiveness of Transfer Learning in Electronic Health Records Data
Sébastien Dubois, Nathanael Romano, Kenneth Jung, Nigam Shah, David C. Kale The Neural Noisy Channel
Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomás Kociský The Preimage of Rectifier Network Activities
Stefan Carlsson, Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan, Kevin Smith Third Person Imitation Learning
Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever Towards a Neural Statistician
Harrison Edwards, Amos J. Storkey Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
Ryan Lowe, Michael Noseworthy, Iulian Vlad Serban, Nicolas Angelard-Gontier, Yoshua Bengio, Joelle Pineau Towards the Limit of Network Quantization
Yoojin Choi, Mostafa El-Khamy, Jungwon Lee Tracking the World State with Recurrent Entity Networks
Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun Trained Ternary Quantization
Chenzhuo Zhu, Song Han, Huizi Mao, William J. Dally Transfer of View-Manifold Learning to Similarity Perception of Novel Objects
Xingyu Lin, Hao Wang, Zhihao Li, Yimeng Zhang, Alan L. Yuille, Tai Sing Lee Trusting SVM for Piecewise Linear CNNs
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar Tuning Recurrent Neural Networks with Reinforcement Learning
Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck Understanding Deep Learning Requires Rethinking Generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals Unrolled Generative Adversarial Networks
Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein Variable Computation in Recurrent Neural Networks
Yacine Jernite, Edouard Grave, Armand Joulin, Tomás Mikolov Variational Intrinsic Control
Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra Variational Lossy Autoencoder
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel Variational Recurrent Adversarial Deep Domain Adaptation
Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, Yan Liu Variational Reference Priors
Eric T. Nalisnick, Padhraic Smyth What Does It Take to Generate Natural Textures?
Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge Words or Characters? Fine-Grained Gating for Reading Comprehension
Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, Ruslan Salakhutdinov Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron C. Courville, Christopher J. Pal