动态 | NIPS 2017 今天开启议程，谷歌科学家竟然组团去了450人，还都不是去玩的！

AI 科技评论按：据说，别人去NIPS 2017是这样的：

而谷歌去NIPS 2017是这样的：

今天，人工智能领域本年度最后一个学术盛会、机器学习领域顶级会议、第31届神经信息处理系统大会（NIPS 2017）就要在加州长滩市开启了。

谷歌作为钻石赞助商，今年共有450人去参加NIPS大会，而我们知道NIPS 2017的参会人数总共有5000+，所以如果你在会场，那么放眼望去，看到的每13个人差不多就有一个是谷歌的人，并且人家这些人还都不是来玩的。

一、活动情况

1、接收论文（Accepted Papers）

据 AI 科技评论了解，今年NIPS会议共有3240篇投稿论文，其中678篇入选（20.9%），40篇orals，112篇spotlights。

在这些入选论文中，国内高校共有19篇论文入选；UC伯克利有16篇，斯坦福有20篇，MIT有20篇，而卡内基·梅隆大学则有高达32篇入选论文。是不是很牛逼？

说真的，并不！

谷歌有45篇入选论文，远超世界顶级的四大高校，更是远超太平洋西岸某一大国的所有高校之和。这里是谷歌入选论文列表：

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

2、Invited talk

NIPS 2017在4-7日期间安排了7场大会报告，其中谷歌作为钻石赞助商，其首席科学家John Platt将在4日下午5：30-6：20做首场invited talk：《Powering the next 100 years》，来讲述谷歌如何使用机器学习来解决未来的能源问题。他是这么说的：

我的梦想就是让地球上的每一个人每年都能够用上和美国普通人一样多的能源。如果实现这个目标，那么在2100年，就需要0.2 x 10^24焦耳的能量，这是非常巨大的。 那么人类文明如何能够获得这么多能量而同时不会导致二氧化碳含量剧增呢？为了回答这个问题，我首先要深入到电力经济学，以了解当前零碳技术的局限性。这些限制也是导致我们仍然在研究如何开发零碳技术（例如核聚变）的原因。对于核聚变，我将说明为什么发展了近70年，对它的开发仍然是一个棘手的问题，而为什么在不久的将来又可能会得到一个很好的解决方案。我还将解释我们如何使用机器学习来优化、加速核聚变的研究。

机器学习+核聚变？有没有突破脑洞极限？

3、会议展示（Conference Demos）

谷歌在NIPS上将有两场会议展示：

1）电子屏保具有高效、强健的移动视觉

Electronic Screen Protector with Efficient and Robust Mobile Vision Hee Jung Ryu, Florian Schroff

在手机上通过人脸进行身份验证，探索的也有一段时间了。但是如何在有很多人的拥挤空间中确定哪张脸是你的呢？

谷歌将在Demos中展示他们开发的DetectGazeNet，识别你只需47ms。

2）Magenta和deeplearn.js：实时控制浏览器中的深度生成音乐模型

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

用深度学习来创作音乐的技术现在越来越成熟了，谷歌的团队将展示如何在浏览器的javascript环境中运行deeplearn.js，从而让用户实时控制这些模型的生成。只需要一个浏览器，自己也能生产音乐，有没有很高端？

4、workshops

所谓workshops，就是在某一主题下若干人一起进行密集讨论的小会。NIPS 2017在8、9号两天一共安排了53个Workshops。谷歌将参加其中的28个。

那么这和自己有什么关系呢？只能说，谷歌的众多大神将在这些workshops闪亮登场，其中就包括那位女神（微笑）。来，看看都认识哪些人……

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

5、座谈会（Symposiums）

NIPS 2017座谈会共4场（12月7日），其中3场有谷歌大牛参与。

1）深化强化学习研讨会

Deep Reinforcement Learning Symposium Authors include: Benjamin Eysenbach, Shane Gu, Julian Ibarz, Sergey Levine

2）可解释的机器学习

Interpretable Machine Learning Authors include: Minmin Chen

3）元学习

Metalearning Organizers include: Quoc V Le

可以说，其中的每一个都是机器学习领域中深之又深的问题。诸位大神们对此的见解或许能刷新自己对机器学习的认识。

哦，对了，另外一场座谈会是：智力的种类 - 类型、测试和满足社会的需求（Kinds Of Intelligence: Types, Tests and Meeting The Needs of Society）

6、比赛（Competitions）

1）对抗攻击防御

Adversarial Attacks and Defences Organizers include: Alexey Kurakin, Ian Goodfellow, Samy Bengio

2）IV竞争：分类临床可操作的基因突变

Competition IV: Classifying Clinically Actionable Genetic Mutations Organizers include: Wendy Kan

7、研讨会（Tutorial）

NIPS 2017共有9场研讨会，谷歌只参加了其中之一：机器学习中的公平性（Fairness in Machine Learning）

Fairness in Machine Learning Solon Barocas, Moritz Hardt

二、有哪些大牛

Samy Bengio

谷歌大脑的研究科学家Samy Bengio是这届大会的程序委员会主席（Program Chair），同时也将参加元学习的研讨会（Workshop on Meta-Learning）以及组织“敌对攻击和防御”（Adversarial Attacks and Defences）的比赛。

Workshop on Meta-Learning Panelists include: Samy Bengio Competitions Adversarial Attacks and Defences Organizers include: Alexey Kurakin, Ian Goodfellow, Samy Bengio

Ian Goodfellow

Ian Goodfellow是本届大会的领域主席。由他组织了“机器欺骗”（Machine Deception）的研讨会，此外他还将在一系列研讨会中做特邀报告/keynote 报告：

Machine Deception Organizers: Ian Goodfellow Invited Speakers include: Ian Goodfellow Machine Learning for Creativity and Design Keynote Speakers include: Ian Goodfellow Machine Learning and Computer Security Invited Speakers include: Ian Goodfellow Aligned Artificial Intelligence Invited Speakers include: Ian Goodfellow Deep Learning: Bridging Theory and Practice Invited Speakers include: Ian Goodfellow Learning with Limited Labeled Data: Weak Supervision and Beyond Invited Speakers include: Ian Goodfellow

除此之外，他还将和Samy Bengio、Alexey Kurakin等人共同组织“对抗攻击防御”（Adversarial Attacks and Defences）的比赛，这个比赛也是Ian Goodfellow所力推的。

Fei-Fei Li

作为国内诸多研究学子心目中的女神，李飞飞在NIPS上的活动相比于前面两位大神则显得有点少，她将出现在8日的这个研讨会中：

Machine Learning for Health (ML4H) Organizers include: Jasper Snoek, Alex Wiltschko Keynote: Fei-Fei Li

记着，中午12点整开讲。

Geoffrey E Hinton

Hinton在本次大会上甚至李飞飞还要低调——只有入选的一篇论文，就是那个火爆一时的《Dynamic Routing Between Capsules》。然而，这篇论文甚至连oral都不是，只有一个5分钟的spotlight。

Dynamic Routing Between Capsules Sara Sabour, Nicholas Frosst, Geoffrey E Hinton