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Google at ICLR 2018

Sunday, April 29, 2018

Posted by Jeff Dean, Google Senior Fellow, Head of Google Research and Machine Intelligence6th International Conference on Learning Representationsmachine learningneural networksdeep learningblueSenior Program Chair:Tara SainathSteering Committee includes:Hugo LarochelleOral ContributionsWasserstein Auto-EncodersIlya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard ScholkopfOn the Convergence of Adam and Beyond(Best Paper Award)Sashank J. Reddi, Satyen Kale, Sanjiv KumarAsk the Right Questions: Active Question Reformulation with Reinforcement LearningChristian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei WangBeyond Word Importance: Contextual Decompositions to Extract Interactions from LSTMsW. James Murdoch, Peter J. Liu, Bin YuConference PostersBoosting the Actor with Dual CriticBo Dai, Albert Shaw, Niao He, Lihong Li, Le SongMaskGAN: Better Text Generation via Filling in the _______William Fedus, Ian Goodfellow, Andrew M. DaiScalable Private Learning with PATENicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Ulfar ErlingssonDeep Gradient Compression: Reducing the Communication Bandwidth for Distributed TrainingYujun Lin, Song Han, Huizi Mao, Yu Wang, William J. DallyFlipout: Efficient Pseudo-Independent Weight Perturbations on Mini-BatchesYeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger GrosseLatent Constraints: Learning to Generate Conditionally from Unconditional Generative ModelsAdam Roberts, Jesse Engel, Matt HoffmanMulti-Mention Learning for Reading Comprehension with Neural CascadesSwabha Swayamdipta, Ankur P. Parikh, Tom KwiatkowskiQANet: Combining Local Convolution with Global Self-Attention for Reading ComprehensionAdams Wei Yu, David Dohan, Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. LeSensitivity and Generalization in Neural Networks: An Empirical StudyRoman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-DicksteinAction-dependent Control Variates for Policy Optimization via Stein IdentityHao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang LiuAn Efficient Framework for Learning Sentence RepresentationsLajanugen Logeswaran, Honglak Lee Fidelity-Weighted LearningMostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf Generating Wikipedia by Summarizing Long SequencesPeter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam ShazeerMatrix Capsules with EM RoutingGeoffrey Hinton, Sara Sabour, Nicholas Frosst Temporal Difference Models: Model-Free Deep RL for Model-Based ControlSergey Levine, Shixiang Gu, Murtaza Dalal, Vitchyr Pong Deep Neural Networks as Gaussian ProcessesJaehoon Lee, Yasaman Bahri, Roman Novak, Samuel L. Schoenholz, Jeffrey Pennington, Jascha Sohl-DicksteinMany Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence at Every StepWilliam Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow Initialization Matters: Orthogonal Predictive State Recurrent Neural NetworksKrzysztof Choromanski, Carlton Downey, Byron BootsLearning Differentially Private Recurrent Language ModelsH. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li ZhangLearning Latent Permutations with Gumbel-Sinkhorn NetworksGonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement LearningBenjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey LevineMeta-Learning for Semi-Supervised Few-Shot ClassificationMengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Josh Tenenbaum, Hugo Larochelle, Richard ZemelThermometer Encoding: One Hot Way to Resist Adversarial ExamplesJacob Buckman, Aurko Roy, Colin Raffel, Ian GoodfellowA Hierarchical Model for Device PlacementAzalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean Monotonic Chunkwise AttentionChung-Cheng Chiu, Colin RaffelTraining Confidence-calibrated Classifiers for Detecting Out-of-Distribution SamplesKimin Lee, Honglak Lee, Kibok Lee, Jinwoo ShinTrust-PCL: An Off-Policy Trust Region Method for Continuous ControlOfir Nachum, Mohammad Norouzi, Kelvin Xu, Dale SchuurmansEnsemble Adversarial Training: Attacks and DefensesFlorian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel Stochastic Variational Video PredictionMohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy Campbell, Sergey LevineDepthwise Separable Convolutions for Neural Machine TranslationLukasz Kaiser, Aidan N. Gomez, Francois CholletDon’t Decay the Learning Rate, Increase the Batch Size Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, Quoc V. LeGenerative Models of Visually Grounded ImaginationRamakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin MurphyLarge Scale Distributed Neural Network Training through Online DistillationRohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. HintonLearning a Neural Response Metric for Retinal ProsthesisNishal P. Shah, Sasidhar Madugula, Alan Litke, Alexander Sher, EJ Chichilnisky, Yoram Singer, Jonathon ShlensNeumann Optimizer: A Practical Optimization Algorithm for Deep Neural NetworksShankar Krishnan, Ying Xiao, Rif A. SaurousA Neural Representation of Sketch DrawingsDavid Ha, Douglas EckDeep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson SamplingCarlos Riquelme, George Tucker, Jasper SnoekGeneralizing Hamiltonian Monte Carlo with Neural NetworksDaniel Levy, Matthew D. Hoffman, Jascha Sohl-DicksteinLeveraging Grammar and Reinforcement Learning for Neural Program SynthesisRudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli On the Discrimination-Generalization Tradeoff in GANsPengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong HeA Bayesian Perspective on Generalization and Stochastic Gradient DescentSamuel L. Smith, Quoc V. Le Learning how to Explain Neural Networks: PatternNet and PatternAttributionPieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven DähneSkip RNN: Learning to Skip State Updates in Recurrent Neural NetworksVíctor Campos, Brendan Jou, Xavier Giró-i-Nieto, Jordi Torres, Shih-Fu ChangTowards Neural Phrase-based Machine TranslationPo-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li DengUnsupervised Cipher Cracking Using Discrete GANsAidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser Variational Image Compression With A Scale HyperpriorJohannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick JohnstonWorkshop PostersLocal Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter ValuesJulius Adebayo, Justin Gilmer, Ian Goodfellow, Been KimStoachastic Gradient Langevin Dynamics that Exploit Neural Network StructureZachary Nado, Jasper Snoek, Bowen Xu, Roger Grosse, David Duvenaud, James MartensTowards Mixed-initiative generation of multi-channel sequential structureAnna Huang, Sherol Chen, Mark J. Nelson, Douglas EckCan Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon KleinbergGILBO: One Metric to Measure Them AllAlexander Alemi, Ian FischerHoME: a Household Multimodal EnvironmentSimon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron CourvilleLearning to Learn without LabelsLuke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-DicksteinLearning via Social Awareness: Improving Sketch Representations with Facial FeedbackNatasha Jaques, Jesse Engel, David Ha, Fred Bertsch, Rosalind Picard, Douglas EckNegative Eigenvalues of the Hessian in Deep Neural Networks Guillaume Alain, Nicolas Le Roux, Pierre-Antoine ManzagolRealistic Evaluation of Semi-Supervised Learning AlgorithmsAvital Oliver, Augustus Odena, Colin Raffel, Ekin Cubuk, lan GoodfellowWinner's Curse? On Pace, Progress, and Empirical Rigor D. Sculley, Jasper Snoek, Alex Wiltschko, Ali RahimiMeta-Learning for Batch Mode Active LearningSachin Ravi, Hugo Larochelle To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model CompressionMichael Zhu, Suyog Gupta Adversarial SpheresJustin Gilmer, Luke Metz, Fartash Faghri, Sam Schoenholz, Maithra Raghu,,Martin Wattenberg, Ian GoodfellowClustering Meets Implicit Generative ModelsFrancesco Locatello, Damien Vincent, Ilya Tolstikhin, Gunnar Ratsch, Sylvain Gelly, Bernhard ScholkopfDecoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity TasksVitalii Zhelezniak, Dan Busbridge, April Shen, Samuel L. Smith, Nils Y. HammerlaLearning Longer-term Dependencies in RNNs with Auxiliary LossesTrieu Trinh, Quoc Le, Andrew Dai, Thang LuongGraph Partition Neural Networks for Semi-Supervised ClassificationAlexander Gaunt, Danny Tarlow, Marc Brockschmidt, Raquel Urtasun, Renjie Liao, Richard ZemelSearching for Activation FunctionsPrajit Ramachandran, Barret Zoph, Quoc LeTime-Dependent Representation for Neural Event Sequence PredictionYang Li, Nan Du, Samy BengioFaster Discovery of Neural Architectures by Searching for Paths in a Large ModelHieu Pham, Melody Guan, Barret Zoph, Quoc V. Le, Jeff DeanIntriguing Properties of Adversarial ExamplesEkin Dogus Cubuk, Barret Zoph, Sam Schoenholz, Quoc Le PPP-Net: Platform-aware Progressive Search for Pareto-optimal Neural ArchitecturesJin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min SunThe Mirage of Action-Dependent Baselines in Reinforcement LearningGeorge Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey LevineLearning to Organize Knowledge with N-Gram MachinesFan Yang, Jiazhong Nie, William W. Cohen, Ni LaoOnline variance-reducing optimizationNicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol