NIPS | 谷歌AI大军来袭，看450多名员工如何横扫今年大会

一年一度的AI盛会NIPS又开始了。

会前数周，就有大神预计，驱车参会的谷歌员工会挤满加州从山景城到长滩的道路，就像这样：

图片来源：杜克大学陈怡然教授微博

但是，NIPS 2017的火爆程度，明显超出了很多人的预期，Twitter网友Mariya拍到的NIPS签到注册的人群，已经从会议中心的一头排到了另一头，而她自己却排在这条长龙的最末端：

与此同时，另一位Twitter网友Dirk Van den Poel则开始抱怨这届大会的组织者明显没有大规模会议的组织经验，光是会前的注册排队就已经乱作一团了：

不过，相比于会议本身的火爆程度，现场的这点拥挤简直就是小巫见大巫。

业已公布的NIPS会议内容中，

DeepMind有16篇论文；而Facebook仅有10篇论文，但有12场Workshop、2场研讨会，并贴心地给出了直播链接https://www.facebook.com/academics；微软研究院的论文要多一些，达到38篇，不过只有2场Workshop；另一家巨头Amazon尽管有4场Workshop，论文却只有6篇……

然而，跟谷歌在今天一早公布的“Google at NIPS 2017”相比，营长瞬间明白了大神在数周前的预测是何其准确：450多位员工、46篇论文、28场Workshop、3场研讨会、2场比赛，这还不算本届大会联席主席Samy Bengio所扮演的角色，及其所组织起来的高达2500人的论文评审队伍。

各家论文合集

DeepMind - https://deepmind.com/blog/deepmind-papers-nips-2017/ Facebook - https://research.fb.com/facebook-research-at-nips-2017/

微软研究院- https://www.microsoft.com/en-us/research/event/microsoft-research-nips-2017/ Amazon - https://www.amazon.jobs/zh/landing_pages/NIPS Google - https://research.googleblog.com/2017/12/google-at-nips-2017.html

谷歌大脑的主管Jeff Dean说他相当期待本周的大会：

谷歌研究社区主编Christian Howard在文章中说，谷歌将在今年的NIPS大会上扮演重要的角色，这话一点都不假。他是这样写的：

本周，第31届神经信息处理系统年会（NIPS2017）将在加州的长滩举办，这是机器学习和计算神经科学领域的一大盛会，内容包括机器学习领域最新的一些座谈会、demo、演讲和报告。Google将在2017年的NIPS大会上扮演重要的角色，450多名Google员工将通过技术演讲、报告、研讨会、比赛和演示等方式发挥作用，并向这个更大的学术研究社区学习。

Google一直处于机器学习领域的最前沿，积极探索从经典算法到深度学习的所有领域。注重理论和应用，特别是那些能够推动整个领域发展的最新技术，包括自然语言理解、语音、翻译、视觉处理和预测等。通过所有这些任务中，我们开发出了用以理解和概括和学习方法，从而给出了我们探讨老问题新视野，并最终改变了我们工作和生活的方式。

如果你正在参加NIPS 2017，我们希望你能够在我们展示台前停一停，跟我们的研究人员聊一聊Google的项目和机会，谈谈那些能够帮助到数十亿人的有趣问题，并观看一些我们正在进行的、激动人心的研究。

当然，你还可以通过下面的列表中仔细了解我们的各项工作。（别忘了，Google还是本届NIPS大会的白金赞助商。）

本届大会组委会：

程序委员会主席: Samy Bengio

Senior Area Chairs include: Corinna Cortes, Dale Schuurmans, Hugo Larochelle

Area Chairs include: Afshin Rostamizadeh, Amir Globerson, Been Kim, D. Sculley, Dumitru Erhan, Gal Chechik, Hartmut Neven, Honglak Lee, Ian Goodfellow, Jasper Snoek, John Wright, Jon Shlens, Kun Zhang, Lihong Li, Maya Gupta, Moritz Hardt, Navdeep Jaitly, Ryan Adams, Sally Goldman, Sanjiv Kumar, Surya Ganguli, Tara Sainath, Umar Syed, Viren Jain, Vitaly Kuznetsov

Invited Talk

Powering the next 100 years

John Platt

论文（46篇）：

A Meta-Learning Perspective on Cold-Start Recommendations for Items

Manasi Vartak, Hugo Larochelle, Arvind Thiagarajan

AdaGAN: Boosting Generative Models

Ilya Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf

Deep Lattice Networks and Partial Monotonic Functions

Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta

From which world is your graph

Cheng Li, Varun Kanade, Felix MF Wong, Zhenming Liu

Hiding Images in Plain Sight: Deep Steganography

Shumeet Baluja

Improved Graph Laplacian via Geometric Self-Consistency

Dominique Joncas, Marina Meila, James McQueen

Model-Powered Conditional Independence Test

Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros Dimakis, Sanjay Shakkottai

Nonlinear random matrix theory for deep learning

Jeffrey Pennington, Pratik Worah

Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice

Jeffrey Pennington, Samuel Schoenholz, Surya Ganguli

SGD Learns the Conjugate Kernel Class of the Network

Amit Daniely

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability

Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein

Learning Hierarchical Information Flow with Recurrent Neural Modules

Danijar Hafner, Alexander Irpan, James Davidson, Nicolas Heess

Online Learning with Transductive Regret

Scott Yang, Mehryar Mohri

Acceleration and Averaging in Stochastic Descent Dynamics

Walid Krichene, Peter Bartlett

Parameter-Free Online Learning via Model Selection

Dylan J Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan

Dynamic Routing Between Capsules

Sara Sabour, Nicholas Frosst, Geoffrey E Hinton

Modulating early visual processing by language

Harm de Vries, Florian Strub, Jeremie Mary, Hugo Larochelle, Olivier Pietquin, Aaron C Courville

MarrNet: 3D Shape Reconstruction via 2.5D Sketches

Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, Josh Tenenbaum

Affinity Clustering: Hierarchical Clustering at Scale

Mahsa Derakhshan, Soheil Behnezhad, Mohammadhossein Bateni, Vahab Mirrokni, MohammadTaghi Hajiaghayi, Silvio Lattanzi, Raimondas Kiveris

Asynchronous Parallel Coordinate Minimization for MAP Inference

Ofer Meshi, Alexander Schwing

Cold-Start Reinforcement Learning with Softmax Policy Gradient

Nan Ding, Radu Soricut

Filtering Variational Objectives

Chris J Maddison, Dieterich Lawson, George Tucker, Mohammad Norouzi,

Nicolas Heess, Andriy Mnih, Yee Whye Teh, Arnaud Doucet

Multi-Armed Bandits with Metric Movement Costs

Tomer Koren, Roi Livni, Yishay Mansour

Multiscale Quantization for Fast Similarity Search

Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel Holtmann-Rice, David Simcha, Felix Yu

Reducing Reparameterization Gradient Variance

Andrew Miller, Nicholas Foti, Alexander D'Amour, Ryan Adams

Statistical Cost Sharing

Eric Balkanski, Umar Syed, Sergei Vassilvitskii

The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings

Krzysztof Choromanski, Mark Rowland, Adrian Weller

Value Prediction Network

Junhyuk Oh, Satinder Singh, Honglak Lee

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

George Tucker, Andriy Mnih, Chris J Maddison, Dieterich Lawson, Jascha Sohl-Dickstein

Approximation and Convergence Properties of Generative Adversarial Learning

Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri

Attention is All you Need

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference

Jonathan Huggins, Ryan Adams, Tamara Broderick

Repeated Inverse Reinforcement Learning

Kareem Amin, Nan Jiang, Satinder Singh

Fair Clustering Through Fairlets

Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii

Affine-Invariant Online Optimization and the Low-rank Experts Problem

Tomer Koren, Roi Livni

Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models

Sergey Ioffe

Bridging the Gap Between Value and Policy Based Reinforcement Learning

Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans

Discriminative State Space Models

Vitaly Kuznetsov, Mehryar Mohri

Dynamic Revenue Sharing

Santiago Balseiro, Max Lin, Vahab Mirrokni, Renato Leme, Song Zuo

Multi-view Matrix Factorization for Linear Dynamical System Estimation

Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvari

On Blackbox Backpropagation and Jacobian Sensing

Krzysztof Choromanski, Vikas Sindhwani

On the Consistency of Quick Shift

Heinrich Jiang

Revenue Optimization with Approximate Bid Predictions

Andres Munoz, Sergei Vassilvitskii

Shape and Material from Sound

Zhoutong Zhang, Qiujia Li, Zhengjia Huang, Jiajun Wu, Josh Tenenbaum, Bill Freeman

Learning to See Physics via Visual De-animation

Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum

Conference Demos

Electronic Screen Protector with Efficient and Robust Mobile Vision

Hee Jung Ryu, Florian Schroff

Magenta and deeplearn.js: Real-time Control of DeepGenerative Music Models in the Browser

Curtis Hawthorne, Ian Simon, Adam Roberts, Jesse Engel, Daniel Smilkov, Nikhil Thorat, Douglas Eck

Workshops

6th Workshop on Automated Knowledge Base Construction (AKBC) 2017

Program Committee includes: Arvind Neelakanta

Authors include: Jiazhong Nie, Ni Lao

Acting and Interacting in the Real World: Challenges in Robot Learning

Invited Speakers include: Pierre Sermanet

Advances in Approximate Bayesian Inference

Panel moderator: Matthew D. Hoffman

Conversational AI - Today's Practice and Tomorrow's Potential

Invited Speakers include: Matthew Henderson, Dilek Hakkani-Tur

Organizers include: Larry Heck

Extreme Classification: Multi-class and Multi-label Learning in Extremely Large Label Spaces

Invited Speakers include: Ed Chi, Mehryar Mohri

Learning in the Presence of Strategic Behavior

Invited Speakers include: Mehryar Mohri

Presenters include: Andres Munoz Medina, Sebastien Lahaie, Sergei Vassilvitskii, Balasubramanian Sivan

Learning on Distributions, Functions, Graphs and Groups

Invited speakers include: Corinna Cortes

Machine Deception

Organizers include: Ian Goodfellow

Invited Speakers include: Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow

Machine Learning and Computer Security

Invited Speakers include: Ian Goodfellow

Organizers include: Nicolas Papernot

Authors include: Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow

Machine Learning for Creativity and Design

Keynote Speakers include: Ian Goodfellow

Organizers include: Doug Eck, David Ha

Machine Learning for Audio Signal Processing (ML4Audio)

Authors include: Aren Jansen, Manoj Plakal, Dan Ellis, Shawn Hershey, Channing Moore, Rif A. Saurous, Yuxuan Wang, RJ Skerry-Ryan, Ying Xiao, Daisy Stanton, Joel Shor, Eric Batternberg, Rob Clark

Machine Learning for Health (ML4H)

Organizers include: Jasper Snoek, Alex Wiltschko

Keynote: Fei-Fei Li

NIPS Time Series Workshop 2017

Organizers include: Vitaly Kuznetsov

Authors include: Brendan Jou

OPT 2017: Optimization for Machine Learning

Organizers include: Sashank Reddi

ML Systems Workshop

Invited Speakers include: Rajat Monga, Alexander Mordvintsev, Chris Olah, Jeff Dean

Authors include: Alex Beutel, Tim Kraska, Ed H. Chi, D. Scully, Michael Terry

Aligned Artificial Intelligence

Invited Speakers include: Ian Goodfellow

Bayesian Deep Learning

Organizers include: Kevin Murphy

Invited speakers include: Nal Kalchbrenner, Matthew D. Hoffman

BigNeuro 2017

Invited speakers include: Viren Jain

Cognitively Informed Artificial Intelligence: Insights From Natural Intelligence

Authors include: Jiazhong Nie, Ni Lao

Deep Learning At Supercomputer Scale

Organizers include: Erich Elsen, Zak Stone, Brennan Saeta, Danijar Haffner

Deep Learning: Bridging Theory and Practice

Invited Speakers include: Ian Goodfellow

Interpreting, Explaining and Visualizing Deep Learning

Invited Speakers include: Been Kim, Honglak Lee

Authors include: Pieter Kinderman, Sara Hooker, Dumitru Erhan, Been Kim

Learning Disentangled Features: from Perception to Control

Organizers include: Honglak Lee

Authors include: Jasmine Hsu, Arkanath Pathak, Abhinav Gupta, James Davidson, Honglak Lee

Learning with Limited Labeled Data: Weak Supervision and Beyond

Invited Speakers include: Ian Goodfellow

Machine Learning on the Phone and other Consumer Devices

Invited Speakers include: Rajat Monga

Organizers include: Hrishikesh Aradhye

Authors include: Suyog Gupta, Sujith Ravi

Optimal Transport and Machine Learning

Organizers include: Olivier Bousquet

The future of gradient-based machine learning software & techniques

Organizers include: Alex Wiltschko, Bart van Merriënboer

Workshop on Meta-Learning

Organizers include: Hugo Larochelle

Panelists include: Samy Bengio

Authors include: Aliaksei Severyn, Sascha Rothe

研讨会：

Deep Reinforcement Learning Symposium

Authors include: Benjamin Eysenbach, Shane Gu, Julian Ibarz, Sergey Levine

Interpretable Machine Learning

Authors include: Minmin Chen

Metalearning

Organizers include: Quoc V Le

比赛：

Adversarial Attacks and Defences

Organizers include: Alexey Kurakin, Ian Goodfellow, Samy Bengio

Competition IV: Classifying Clinically Actionable Genetic Mutations

Organizers include: Wendy Kan

Tutorial

Fairness in Machine Learning

Solon Barocas, Moritz Hardt