动态 | Ian Goodfellow撰文总结：谷歌的 ICLR 2017 硕果累累

2017 年 4 月 25 日 AI科技评论

谷歌大脑团队的 Ian Goodfellow 今日在研究院官网上撰文，总结了谷歌在 ICLR 2017 上所做的学术贡献。AI科技评论编译全文如下，未经许可不得转载。

本周，第五届国际学习表征会议（ICLR 2017）在法国土伦召开，这是一个关注机器学习领域如何从数据中习得具有意义及有用表征的会议。ICLR 包括 conference track 及 workshop track 两个项目，邀请了获得 oral 及 poster 的研究者们进行分享，涵盖深度学习、度量学习、核学习、组合模型、非线性结构化预测，及非凸优化问题。

站在神经网络及深度学习领域浪潮之巅，谷歌关注理论与实践，并致力于开发理解与总结的学习方法。作为 ICLR 2017 的白金赞助商，谷歌有超过 50 名研究者出席本次会议（大部分为谷歌大脑团队及谷歌欧洲研究分部的成员），通过在现场展示论文及海报的方式，为建设一个更完善的学术研究交流平台做出了贡献，也是一个互相学习的过程。此外，谷歌的研究者也是 workshops 及组委会构建的中坚力量。

如果你来到 ICLR 2017，我们希望你能在我们的展台前驻足，并与我们的研究者进行交流，探讨如何为数十亿人解决有趣的问题。

以下为谷歌在 ICLR 2017 展示的论文内容（其中的谷歌研究者已经加粗表示）

区域主席

George Dahl, Slav Petrov, Vikas Sindhwani

程序主席

Hugo Larochelle, Tara Sainath

受邀演讲论文

Understanding Deep Learning Requires Rethinking Generalization (Best Paper Award)
Chiyuan Zhang*, Samy Bengio, Moritz Hardt, Benjamin Recht*, Oriol Vinyals
Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data (Best Paper Award)
Nicolas Papernot*, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, Kunal Talwar
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
Shixiang (Shane) Gu*, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine
Neural Architecture Search with Reinforcement Learning
Barret Zoph, Quoc Le

Poster 论文

Adversarial Machine Learning at Scale
Alexey Kurakin, Ian J. Goodfellow†, Samy Bengio
Capacity and Trainability in Recurrent Neural Networks
Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo
Improving Policy Gradient by Exploring Under-Appreciated Rewards
Ofir Nachum, Mohammad Norouzi, Dale Schuurmans
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean
Unrolled Generative Adversarial Networks
Luke Metz, Ben Poole*, David Pfau, Jascha Sohl-Dickstein
Categorical Reparameterization with Gumbel-Softmax
Eric Jang, Shixiang (Shane) Gu*, Ben Poole*
Decomposing Motion and Content for Natural Video Sequence Prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, Honglak Lee
Density Estimation Using Real NVP
Laurent Dinh*, Jascha Sohl-Dickstein, Samy Bengio
Latent Sequence Decompositions
William Chan*, Yu Zhang*, Quoc Le, Navdeep Jaitly*
Learning a Natural Language Interface with Neural Programmer
Arvind Neelakantan*, Quoc V. Le, Martín Abadi, Andrew McCallum*, Dario Amodei*
Deep Information Propagation
Samuel Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein
Identity Matters in Deep Learning
Moritz Hardt, Tengyu Ma
A Learned Representation For Artistic Style
Vincent Dumoulin*, Jonathon Shlens, Manjunath Kudlur
Adversarial Training Methods for Semi-Supervised Text Classification
Takeru Miyato, Andrew M. Dai, Ian Goodfellow†
HyperNetworks
David Ha, Andrew Dai, Quoc V. Le
Learning to Remember Rare Events
Lukasz Kaiser, Ofir Nachum, Aurko Roy*, Samy Bengio

Workshop Track

Particle Value Functions
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
Neural Combinatorial Optimization with Reinforcement Learning
Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, Samy Bengio
Short and Deep: Sketching and Neural Networks
Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar
Explaining the Learning Dynamics of Direct Feedback Alignment
Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein
Training a Subsampling Mechanism in Expectation
Colin Raffel, Dieterich Lawson
Tuning Recurrent Neural Networks with Reinforcement Learning
Natasha Jaques*, Shixiang (Shane) Gu*, Richard E. Turner, Douglas Eck
REBAR: Low-Variance, Unbiased Gradient Estimates for Discrete Latent Variable Models
George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein
Adversarial Examples in the Physical World
Alexey Kurakin, Ian Goodfellow†, Samy Bengio
Regularizing Neural Networks by Penalizing Confident Output Distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey Hinton
Unsupervised Perceptual Rewards for Imitation Learning
Pierre Sermanet, Kelvin Xu, Sergey Levine
Changing Model Behavior at Test-time Using Reinforcement Learning
Augustus Odena, Dieterich Lawson, Christopher Olah