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| 本文作者： 奕欣 | 2017-04-25 16:14 | 專題：ICLR 2017 |
雷鋒網(wǎng)消息，谷歌大腦團(tuán)隊的 Ian Goodfellow 今日在研究院官網(wǎng)上撰文，總結(jié)了谷歌在 ICLR 2017 上所做的學(xué)術(shù)貢獻(xiàn)。雷鋒網(wǎng)編譯全文如下，未經(jīng)許可不得轉(zhuǎn)載。
本周，第五屆國際學(xué)習(xí)表征會議（ICLR 2017）在法國土倫召開，這是一個關(guān)注機器學(xué)習(xí)領(lǐng)域如何從數(shù)據(jù)中習(xí)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(jìn)行分享，涵蓋深度學(xué)習(xí)、度量學(xué)習(xí)、核學(xué)習(xí)、組合模型、非線性結(jié)構(gòu)化預(yù)測，及非凸優(yōu)化問題。
站在神經(jīng)網(wǎng)絡(luò)及深度學(xué)習(xí)領(lǐng)域浪潮之巔，谷歌關(guān)注理論與實踐，并致力于開發(fā)理解與總結(jié)的學(xué)習(xí)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(tuán)隊及谷歌歐洲研究分部的成員），通過在現(xiàn)場展示論文及海報的方式，為建設(shè)一個更完善的學(xué)術(shù)研究交流平臺做出了貢獻(xiàn)，也是一個互相學(xué)習(xí)的過程。此外，谷歌的研究者也是 workshops 及組委會構(gòu)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(jìn)行交流，探討如何為數(shù)十億人解決有趣的問題。
以下為谷歌在 ICLR 2017 展示的論文內(nèi)容（其中的谷歌研究者已經(jīng)加粗表示）
George Dahl, Slav Petrov, Vikas Sindhwani
Hugo Larochelle, Tara Sainath
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
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
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(nèi)容在谷歌就職時完成
? 工作內(nèi)容在 OpenAI 任職時完成
詳細(xì)信息可訪問 research.googleblog 了解，雷鋒網(wǎng)編譯。
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