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ORCID6th ICLR 2018: Vancouver, BC, Canada
- 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net 2018
Oral Papers
- Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
On the Convergence of Adam and Beyond. - Yonatan Belinkov, Yonatan Bisk:
Synthetic and Natural Noise Both Break Neural Machine Translation. - Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger:
Multi-Scale Dense Networks for Resource Efficient Image Classification. - Shuang Wu, Guoqi Li, Feng Chen, Luping Shi:
Training and Inference with Integers in Deep Neural Networks. - Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls, Stephen Clark:
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input. - Taco S. Cohen, Mario Geiger, Jonas Köhler
, Max Welling:
Spherical CNNs. - Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang:
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning. - Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the insufficiency of existing momentum schemes for Stochastic Optimization. - Aman Sinha, Hongseok Namkoong, John C. Duchi:
Certifying Some Distributional Robustness with Principled Adversarial Training. - Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, Huan Xu, Hongyuan Zha:
Learning Deep Mean Field Games for Modeling Large Population Behavior. - Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf:
Wasserstein Auto-Encoders. - Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida:
Spectral Normalization for Generative Adversarial Networks. - Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi:
Learning to Represent Programs with Graphs. - Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi N. R. Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E. Houle, James Bailey:
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality. - Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W. Cohen:
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model. - Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel:
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments. - Nadav Cohen, Ronen Tamari, Amnon Shashua:
Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions. - Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine:
Neural Sketch Learning for Conditional Program Generation. - Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen:
Progressive Growing of GANs for Improved Quality, Stability, and Variation. - Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham M. Kakade, Igor Mordatch, Pieter Abbeel:
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines. - Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell:
Zero-Shot Visual Imitation. - W. James Murdoch, Peter J. Liu, Bin Yu:
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs. - Ashish Bora, Eric Price, Alexandros G. Dimakis:
AmbientGAN: Generative models from lossy measurements.
Poster Papers
- Pietro Morerio, Jacopo Cavazza, Vittorio Murino:
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation. - Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel:
Large Scale Optimal Transport and Mapping Estimation. - Wen Sun, J. Andrew Bagnell, Byron Boots:
Truncated horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. - Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel:
Model-Ensemble Trust-Region Policy Optimization. - David Ha, Douglas Eck:
A Neural Representation of Sketch Drawings. - Thorsten Joachims, Adith Swaminathan, Maarten de Rijke:
Deep Learning with Logged Bandit Feedback. - Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek:
Learning Latent Permutations with Gumbel-Sinkhorn Networks. - Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin A. Riedmiller:
Learning an Embedding Space for Transferable Robot Skills. - Alexandre Péré, Sébastien Forestier, Olivier Sigaud, Pierre-Yves Oudeyer:
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration. - Mickaël Chen, Ludovic Denoyer, Thierry Artières:
Multi-View Data Generation Without View Supervision. - Carlos Riquelme, George Tucker, Jasper Snoek:
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. - Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann:
Semantic Interpolation in Implicit Models. - Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf:
Fidelity-Weighted Learning. - Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg:
Latent Space Oddity: on the Curvature of Deep Generative Models. - Aviv Tamar, Khashayar Rohanimanesh, Yinlam Chow, Chris Vigorito, Ben Goodrich, Michael Kahane, Derik Pridmore:
Imitation Learning from Visual Data with Multiple Intentions. - Elad Hazan, Adam R. Klivans, Yang Yuan:
Hyperparameter optimization: a spectral approach. - Rudy Bunel, Matthew J. Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli:
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis. - Xingyu Liu, Jeff Pool, Song Han, William J. Dally:
Efficient Sparse-Winograd Convolutional Neural Networks. - Fabrizio Pedersoli, George Tzanetakis, Andrea Tagliasacchi:
Espresso: Efficient Forward Propagation for Binary Deep Neural Networks. - Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang, Hao Li:
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis. - Ricky Fok, Aijun An, Zana Rashidi, Xiaogang Wang:
Decoupling the Layers in Residual Networks. - Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou, Kostas Daniilidis:
Polar Transformer Networks. - Shiyu Liang, Yixuan Li, R. Srikant:
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks. - Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein:
Stabilizing Adversarial Nets with Prediction Methods. - Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio:
Graph Attention Networks. - Tianyi Zhou, Jeff A. Bilmes:
Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity. - Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein:
Generalizing Hamiltonian Monte Carlo with Neural Networks. - Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause:
An Online Learning Approach to Generative Adversarial Networks. - Tim Salimans, Han Zhang, Alec Radford, Dimitris N. Metaxas:
Improving GANs Using Optimal Transport. - Yan Wu, Greg Wayne, Alex Graves, Timothy P. Lillicrap:
The Kanerva Machine: A Generative Distributed Memory. - Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory F. Diamos, Erich Elsen, David García, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu:
Mixed Precision Training. - Jesse H. Engel, Matthew D. Hoffman, Adam Roberts:
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models. - William Fedus, Ian J. Goodfellow, Andrew M. Dai:
MaskGAN: Better Text Generation via Filling in the _______. - Alex Nowak, David Folqué, Joan Bruna:
Divide and Conquer Networks. - Chelsea Finn, Sergey Levine:
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm. - Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rémi Munos, Nicolas Heess, Martin A. Riedmiller:
Maximum a Posteriori Policy Optimisation. - Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman:
Meta Learning Shared Hierarchies. - Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein:
Deep Neural Networks as Gaussian Processes. - Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song:
Syntax-Directed Variational Autoencoder for Structured Data. - Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, Sumit Gulwani:
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples. - Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell:
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering. - Asit K. Mishra, Eriko Nurvitadhi, Jeffrey J. Cook, Debbie Marr:
WRPN: Wide Reduced-Precision Networks. - Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung:
MGAN: Training Generative Adversarial Nets with Multiple Generators. - Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos:
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. - Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, Simon Lacoste-Julien:
SEARNN: Training RNNs with global-local losses. - Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap:
Distributed Distributional Deterministic Policy Gradients. - Adam Christopher Earle, Andrew M. Saxe, Benjamin Rosman:
Hierarchical Subtask Discovery with Non-Negative Matrix Factorization. - Roy Fox, Richard Shin, Sanjay Krishnan, Ken Goldberg, Dawn Song, Ion Stoica:
Parametrized Hierarchical Procedures for Neural Programming. - Dongsoo Lee, Daehyun Ahn, Taesu Kim, Pierce I-Jen Chuang, Jae-Joon Kim:
Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio. - Takeru Miyato, Masanori Koyama:
cGANs with Projection Discriminator. - Spyros Gidaris, Praveer Singh, Nikos Komodakis:
Unsupervised Representation Learning by Predicting Image Rotations. - Katrina Evtimova, Andrew Drozdov, Douwe Kiela, Kyunghyun Cho:
Emergent Communication in a Multi-Modal, Multi-Step Referential Game. - Jie Chen, Tengfei Ma, Cao Xiao:
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. - Jason Lee, Kyunghyun Cho, Jason Weston, Douwe Kiela:
Emergent Translation in Multi-Agent Communication. - Lajanugen Logeswaran, Honglak Lee:
An efficient framework for learning sentence representations. - Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler:
NerveNet: Learning Structured Policy with Graph Neural Networks. - Ozsel Kilinc, Ismail Uysal:
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization. - Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, Kate Saenko:
Adversarial Dropout Regularization. - Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton:
Demystifying MMD GANs. - Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Smooth Loss Functions for Deep Top-k Classification. - Abram L. Friesen, Pedro M. Domingos:
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem. - Lifu Tu, Kevin Gimpel:
Learning Approximate Inference Networks for Structured Prediction. - Dejiao Zhang, Haozhu Wang, Mário A. T. Figueiredo, Laura Balzano:
Learning to Share: simultaneous parameter tying and Sparsification in Deep Learning. - Antonio Polino, Razvan Pascanu, Dan Alistarh:
Model compression via distillation and quantization. - Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan:
Variational Message Passing with Structured Inference Networks. - Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu:
Action-dependent Control Variates for Policy Optimization via Stein Identity. - Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston:
Variational image compression with a scale hyperprior. - Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan:
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations. - Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse:
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. - Jiaxin Shi, Shengyang Sun, Jun Zhu:
Kernel Implicit Variational Inference. - Hippolyt Ritter, Aleksandar Botev, David Barber:
A Scalable Laplace Approximation for Neural Networks. - Alexander G. Anderson, Cory P. Berg:
The High-Dimensional Geometry of Binary Neural Networks. - Asit K. Mishra, Debbie Marr:
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy. - Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver:
Distributed Prioritized Experience Replay. - Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada:
Learning from Between-class Examples for Deep Sound Recognition. - Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin:
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. - Yaniv Taigman, Lior Wolf, Adam Polyak, Eliya Nachmani:
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop. - Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton:
Large scale distributed neural network training through online distillation. - H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang:
Learning Differentially Private Recurrent Language Models. - Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston:
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent. - Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer:
Generating Wikipedia by Summarizing Long Sequences. - Guillaume Lample, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato:
Unsupervised Machine Translation Using Monolingual Corpora Only. - Romain Paulus, Caiming Xiong, Richard Socher:
A Deep Reinforced Model for Abstractive Summarization. - Raphael Shu, Hideki Nakayama:
Compressing Word Embeddings via Deep Compositional Code Learning. - Yujun Lin, Song Han, Huizi Mao, Yu Wang, Bill Dally:
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. - Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le:
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension. - Mikel Artetxe, Gorka Labaka, Eneko Agirre, Kyunghyun Cho:
Unsupervised Neural Machine Translation. - Rong Ge, Jason D. Lee, Tengyu Ma:
Learning One-hidden-layer Neural Networks with Landscape Design. - Yi Zhou, Yingbin Liang:
Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties. - Sergio Valcarcel Macua, Javier Zazo, Santiago Zazo:
Learning Parametric Closed-Loop Policies for Markov Potential Games. - David Rolnick, Max Tegmark:
The power of deeper networks for expressing natural functions. - Pan Zhou, Jiashi Feng:
Empirical Risk Landscape Analysis for Understanding Deep Neural Networks. - Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He:
On the Discrimination-Generalization Tradeoff in GANs. - Wieland Brendel, Jonas Rauber, Matthias Bethge:
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. - Corentin Tallec, Yann Ollivier:
Unbiased Online Recurrent Optimization. - Chunyuan Li, Heerad Farkhoor, Rosanne Liu, Jason Yosinski:
Measuring the Intrinsic Dimension of Objective Landscapes. - Youngjin Kim, Minjung Kim, Gunhee Kim:
Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks. - Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Animashree Anandkumar:
Stochastic Activation Pruning for Robust Adversarial Defense. - Feiwen Zhu, Jeff Pool, Michael Andersch, Jeremy Appleyard, Fung Xie:
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip. - Jinsung Yoon, James Jordon, Mihaela van der Schaar:
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets. - Jacob Buckman, Aurko Roy, Colin Raffel, Ian J. Goodfellow:
Thermometer Encoding: One Hot Way To Resist Adversarial Examples. - Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans:
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control. - Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, Sergey Levine:
Stochastic Variational Video Prediction. - Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool:
Towards Image Understanding from Deep Compression Without Decoding. - Sungyong Seo, Arash Mohegh, George Ban-Weiss, Yan Liu:
Automatically Inferring Data Quality for Spatiotemporal Forecasting. - Marco Ancona, Enea Ceolini, Cengiz Öztireli, Markus Gross:
Towards better understanding of gradient-based attribution methods for Deep Neural Networks. - Chuan Guo, Mayank Rana, Moustapha Cissé, Laurens van der Maaten:
Countering Adversarial Images using Input Transformations.