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！

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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！

2017 年 12 月 4 日 AI科技评论 camel

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