【ICLR 2018】Google 研究盘点,76篇论文抢先看

本周,素有深度学习届顶会“无冕之王”之称的ICLR 2018第6届在加拿大温哥华举办,会议的重点是如何学习有意义的有用的机器学习数据表示。 ICLR包括会议和workshop,以及关于深度学习,度量学习,核学习,组合模型,非线性结构预测以及有关非凸优化的一些最新研究的演讲和海报展示。谷歌、DeepMind等大厂这几天陆续公布了今年的论文,全是干货。

作为神经网络和深度学习顶尖技术创新的领先者,Google专注于理论和应用。作为ICLR 2018的白金赞助商,Google将有超过130名研究人员参加会议,分享论文,参与workshop。

ICLR 2018为期4天,5月3日结束。与往年一样,本次大会每天分上午下午两场。每场形式基本一样,先是邀请演讲(invited talk),然后是讨论,也就是被选为能够进行口头(Oral)的论文、茶歇、海报展示(poster)。

如果您正在参加ICLR 2018,我们希望您能够通过我们的展位停下来,与我们的研究人员聊聊Google为数十亿人解决问题的项目和机会。您还可以在下面的列表中了解更多关于我们在ICLR 2018上展示的研究成果(Google员工加粗显示

资深程序主席包括:

Tara Sainath

核心委员包括:

Hugo Larochelle

Google ICLR 2018论文集



口头报告(Oral Contributions)

Wasserstein Auto-Encoders Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Scholkopf On the Convergence of Adam and Beyond (Best Paper Award) Sashank J. Reddi, Satyen Kale, Sanjiv Kumar Ask the Right Questions: Active Question Reformulation with Reinforcement Learning Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang Beyond Word Importance: Contextual Decompositions to Extract Interactions from LSTMs W. James Murdoch, Peter J. Liu, Bin Yu 会议海报(Conference Posters) Boosting the Actor with Dual Critic Bo Dai, Albert Shaw, Niao He, Lihong Li, Le Song MaskGAN: Better Text Generation via Filling in the _______ William Fedus, Ian Goodfellow, Andrew M. Dai Scalable Private Learning with PATE Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Ulfar Erlingsson Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Yujun Lin, Song Han, Huizi Mao, Yu Wang, William J. Dally Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models Adam Roberts, Jesse Engel, Matt Hoffman Multi-Mention Learning for Reading Comprehension with Neural Cascades Swabha Swayamdipta, Ankur P. Parikh, Tom Kwiatkowski QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension Adams Wei Yu, David Dohan, Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le Sensitivity and Generalization in Neural Networks: An Empirical Study Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein Action-dependent Control Variates for Policy Optimization via Stein Identity Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu An Efficient Framework for Learning Sentence Representations Lajanugen Logeswaran, Honglak Lee Fidelity-Weighted Learning Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf Generating Wikipedia by Summarizing Long Sequences Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer Matrix Capsules with EM Routing Geoffrey Hinton, Sara Sabour, Nicholas Frosst Temporal Difference Models: Model-Free Deep RL for Model-Based Control Sergey Levine, Shixiang Gu, Murtaza Dalal, Vitchyr Pong Deep Neural Networks as Gaussian Processes Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel L. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence at Every Step William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow Initialization Matters: Orthogonal Predictive State Recurrent Neural Networks Krzysztof Choromanski, Carlton Downey, Byron Boots Learning Differentially Private Recurrent Language Models H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang Learning Latent Permutations with Gumbel-Sinkhorn Networks Gonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine Meta-Learning for Semi-Supervised Few-Shot Classification Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Josh Tenenbaum, Hugo Larochelle, Richard Zemel Thermometer Encoding: One Hot Way to Resist Adversarial Examples Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow A Hierarchical Model for Device Placement Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean Monotonic Chunkwise Attention Chung-Cheng Chiu, Colin Raffel Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin Trust-PCL: An Off-Policy Trust Region Method for Continuous Control Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel Stochastic Variational Video Prediction Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy Campbell, Sergey Levine Depthwise Separable Convolutions for Neural Machine Translation Lukasz Kaiser, Aidan N. Gomez, Francois Chollet Don’t Decay the Learning Rate, Increase the Batch Size Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, Quoc V. Le Generative Models of Visually Grounded Imagination Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy Large Scale Distributed Neural Network Training through Online Distillation Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton Learning a Neural Response Metric for Retinal Prosthesis Nishal P. Shah, Sasidhar Madugula, Alan Litke, Alexander Sher, EJ Chichilnisky, Yoram Singer,Jonathon Shlens Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks Shankar Krishnan, Ying Xiao, Rif A. Saurous A Neural Representation of Sketch Drawings David Ha, Douglas Eck Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling Carlos Riquelme, George Tucker, Jasper Snoek Generalizing Hamiltonian Monte Carlo with Neural Networks Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli On the Discrimination-Generalization Tradeoff in GANs Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He A Bayesian Perspective on Generalization and Stochastic Gradient Descent Samuel L. Smith, Quoc V. Le Learning how to Explain Neural Networks: PatternNet and PatternAttribution Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks Víctor Campos, Brendan Jou, Xavier Giró-i-Nieto, Jordi Torres, Shih-Fu Chang Towards Neural Phrase-based Machine Translation Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng Unsupervised Cipher Cracking Using Discrete GANs Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser Variational Image Compression With A Scale Hyperprior Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston Workshop Posters Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo, Justin Gilmer, Ian Goodfellow, Been Kim Stoachastic Gradient Langevin Dynamics that Exploit Neural Network Structure Zachary Nado, Jasper Snoek, Bowen Xu, Roger Grosse, David Duvenaud, James Martens Towards Mixed-initiative generation of multi-channel sequential structure Anna Huang, Sherol Chen, Mark J. Nelson, Douglas Eck Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon Kleinberg GILBO: One Metric to Measure Them All Alexander Alemi, Ian Fischer HoME: a Household Multimodal Environment Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat,Hugo Larochelle, Aaron Courville Learning to Learn without Labels Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein Learning via Social Awareness: Improving Sketch Representations with Facial Feedback Natasha Jaques, Jesse Engel, David Ha, Fred Bertsch, Rosalind Picard, Douglas Eck Negative Eigenvalues of the Hessian in Deep Neural Networks Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol Realistic Evaluation of Semi-Supervised Learning Algorithms Avital Oliver, Augustus Odena, Colin Raffel, Ekin Cubuk, lan Goodfellow Winner's Curse? On Pace, Progress, and Empirical Rigor D. Sculley, Jasper Snoek, Alex Wiltschko, Ali Rahimi Meta-Learning for Batch Mode Active Learning Sachin Ravi, Hugo Larochelle To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression Michael Zhu, Suyog Gupta Adversarial Spheres Justin Gilmer, Luke Metz, Fartash Faghri, Sam Schoenholz, Maithra Raghu,,Martin Wattenberg, Ian Goodfellow Clustering Meets Implicit Generative Models Francesco Locatello, Damien Vincent, Ilya Tolstikhin, Gunnar Ratsch, Sylvain Gelly, Bernhard Scholkopf Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks Vitalii Zhelezniak, Dan Busbridge, April Shen, Samuel L. Smith, Nils Y. Hammerla Learning Longer-term Dependencies in RNNs with Auxiliary Losses Trieu Trinh, Quoc Le, Andrew Dai, Thang Luong Graph Partition Neural Networks for Semi-Supervised Classification Alexander Gaunt, Danny Tarlow, Marc Brockschmidt, Raquel Urtasun, Renjie Liao, Richard Zemel Searching for Activation Functions Prajit Ramachandran, Barret Zoph, Quoc Le Time-Dependent Representation for Neural Event Sequence Prediction Yang Li, Nan Du, Samy Bengio Faster Discovery of Neural Architectures by Searching for Paths in a Large Model Hieu Pham, Melody Guan, Barret Zoph, Quoc V. Le, Jeff Dean Intriguing Properties of Adversarial Examples Ekin Dogus Cubuk, Barret Zoph, Sam Schoenholz, Quoc Le PPP-Net: Platform-aware Progressive Search for Pareto-optimal Neural Architectures Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun The Mirage of Action-Dependent Baselines in Reinforcement Learning George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine Learning to Organize Knowledge with N-Gram Machines Fan Yang, Jiazhong Nie, William W. Cohen, Ni Lao Online variance-reducing optimization Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol

参考链接:

https://research.googleblog.com/2018/04/google-at-iclr-2018.html

https://iclr.cc/Conferences/2018/ScheduleOverview

-END-

原文发布于微信公众号 - 专知(Quan_Zhuanzhi)

原文发表时间:2018-04-30

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