Ian Goodfellow撰文總結：谷歌的 ICLR 2017 碩果累累

雷鋒網消息，谷歌大腦團隊的 Ian Goodfellow 今日在研究院官網上撰文，總結了谷歌在 ICLR 2017 上所做的學術貢獻。雷鋒網編譯全文如下，未經許可不得轉載。

本周，第五屆國際學習表征會議（ICLR 2017）在法國土倫召開，這是一個關注機器學習領域如何從數據中習得具有意義及有用表征的會議。ICLR 包括 conference track 及 workshop track 兩個項目，邀請了獲得 oral 及 poster 的研究者們進行分享，涵蓋深度學習、度量學習、核學習、組合模型、非線性結構化預測，及非凸優化問題。

站在神經網絡及深度學習領域浪潮之巔，谷歌關注理論與實踐，并致力于開發理解與總結的學習方法。作為 ICLR 2017 的白金贊助商，谷歌有超過 50 名研究者出席本次會議（大部分為谷歌大腦團隊及谷歌歐洲研究分部的成員），通過在現場展示論文及海報的方式，為建設一個更完善的學術研究交流平臺做出了貢獻，也是一個互相學習的過程。此外，谷歌的研究者也是 workshops 及組委會構建的中堅力量。

如果你來到 ICLR 2017，我們希望你能在我們的展臺前駐足，并與我們的研究者進行交流，探討如何為數十億人解決有趣的問題。

以下為谷歌在 ICLR 2017 展示的論文內容（其中的谷歌研究者已經加粗表示）

區域主席

George Dahl, Slav Petrov, Vikas Sindhwani

程序主席（雷鋒網此前已經做過相關介紹）

Hugo Larochelle, Tara Sainath

受邀演講論文

• 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 論文

• 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

* 工作內容在谷歌就職時完成

? 工作內容在 OpenAI 任職時完成

詳細信息可訪問 research.googleblog 了解，雷鋒網編譯。