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| 本文作者： camel | 編輯：郭奕欣 | 2017-12-04 17:52 | 專題：NIPS 2017 |
據(jù)說，別人去NIPS 2017是這樣的：
谷歌去NIPS 2017是這樣的：
雷鋒網(wǎng)AI科技評論按：今天，人工智能領(lǐng)域本年度最后一個學(xué)術(shù)盛會、機(jī)器學(xué)習(xí)領(lǐng)域頂級會議、第31屆神經(jīng)信息處理系統(tǒng)大會（NIPS 2017）就要在加州長灘市開啟了。（雷鋒網(wǎng)AI科技評論記者也將親臨現(xiàn)場進(jìn)行全程報道?。?/p>
谷歌作為鉆石贊助商，今年共有450人去參加NIPS大會，而我們知道NIPS 2017的參會人數(shù)總共有5000+，所以如果你在會場，那么放眼望去，看到的每13個人差不多就有一個是谷歌的人，并且人家這些人還都不是來玩的。
據(jù)雷鋒網(wǎng)了解，今年NIPS會議共有3240篇投稿論文，其中678篇入選（20.9%），40篇orals，112篇spotlights。
在這些入選論文中，國內(nèi)高校共有19篇論文入選；UC伯克利有16篇，斯坦福有20篇，MIT有20篇，而卡內(nèi)基·梅隆大學(xué)則有高達(dá)32篇入選論文。是不是很牛逼？
說真的，并不！
谷歌有45篇入選論文，遠(yuǎn)超世界頂級的四大高校，更是遠(yuǎn)超太平洋西岸某一大國的所有高校之和。這里是谷歌入選論文列表：
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 Sch?lkopf
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, ?ukasz 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
NIPS 2017在4-7日期間安排了7場大會報告，其中谷歌作為鉆石贊助商，其首席科學(xué)家John Platt將在4日下午5：30-6：20做首場invited talk：《Powering the next 100 years》，來講述谷歌如何使用機(jī)器學(xué)習(xí)來解決未來的能源問題。他是這么說的：
我的夢想就是讓地球上的每一個人每年都能夠用上和美國普通人一樣多的能源。如果實現(xiàn)這個目標(biāo)，那么在2100年，就需要0.2 x 10^24焦耳的能量，這是非常巨大的。
那么人類文明如何能夠獲得這么多能量而同時不會導(dǎo)致二氧化碳含量劇增呢？為了回答這個問題，我首先要深入到電力經(jīng)濟(jì)學(xué)，以了解當(dāng)前零碳技術(shù)的局限性。這些限制也是導(dǎo)致我們?nèi)匀辉谘芯咳绾伍_發(fā)零碳技術(shù)（例如核聚變）的原因。對于核聚變，我將說明為什么發(fā)展了近70年，對它的開發(fā)仍然是一個棘手的問題，而為什么在不久的將來又可能會得到一個很好的解決方案。我還將解釋我們?nèi)绾问褂脵C(jī)器學(xué)習(xí)來優(yōu)化、加速核聚變的研究。
啥，機(jī)器學(xué)習(xí)+核聚變？是的，是不是很突破腦洞極限？
谷歌在NIPS上將有兩場會議展示：
1）電子屏保具有高效、強(qiáng)健的移動視覺
Electronic Screen Protector with Efficient and Robust Mobile Vision
Hee Jung Ryu, Florian Schroff
在手機(jī)上通過人臉進(jìn)行身份驗證，探索的也有一段時間了。但是如何在有很多人的擁擠空間中確定哪張臉是你的呢？
谷歌將在Demos中展示他們開發(fā)的DetectGazeNet，識別你只需47ms。
2）Magenta和deeplearn.js：實時控制瀏覽器中的深度生成音樂模型
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
用深度學(xué)習(xí)來創(chuàng)作音樂的技術(shù)現(xiàn)在越來越成熟了，谷歌的團(tuán)隊將展示如何在瀏覽器的javascript環(huán)境中運行deeplearn.js，從而讓用戶實時控制這些模型的生成。只需要一個瀏覽器，自己也能生產(chǎn)音樂，有沒有很高端？
所謂workshops，就是在某一主題下若干人一起進(jìn)行密集討論的小會。NIPS 2017在8、9號兩天一共安排了53個Workshops。谷歌將參加其中的28個。
那么這和自己有什么關(guān)系呢？只能說，谷歌的眾多大神將在這些workshops閃亮登場，其中就包括那位女神（微笑）。來，看看都認(rèn)識哪些人……
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 Merri?nboer
Workshop on Meta-Learning
Organizers include: Hugo Larochelle
Panelists include: Samy Bengio
Authors include: Aliaksei Severyn, Sascha Rothe
NIPS 2017座談會共4場（12月7日），其中3場有谷歌大牛參與。
1）深化強(qiáng)化學(xué)習(xí)研討會
Deep Reinforcement Learning Symposium
Authors include: Benjamin Eysenbach, Shane Gu, Julian Ibarz, Sergey Levine
2）可解釋的機(jī)器學(xué)習(xí)
Interpretable Machine Learning
Authors include: Minmin Chen
3）元學(xué)習(xí)
Metalearning
Organizers include: Quoc V Le
可以說，其中的每一個都是機(jī)器學(xué)習(xí)領(lǐng)域中深之又深的問題。諸位大神們對此的見解或許能刷新自己對機(jī)器學(xué)習(xí)的認(rèn)識。
哦，對了，另外一場座談會是：智力的種類 - 類型、測試和滿足社會的需求（Kinds Of Intelligence: Types, Tests and Meeting The Needs of Society）
1）對抗攻擊防御
Adversarial Attacks and Defences
Organizers include: Alexey Kurakin, Ian Goodfellow, Samy Bengio
2）IV競爭：分類臨床可操作的基因突變
Competition IV: Classifying Clinically Actionable Genetic Mutations
Organizers include: Wendy Kan
NIPS 2017共有9場研討會，谷歌只參加了其中之一：機(jī)器學(xué)習(xí)中的公平性（Fairness in Machine Learning）
Fairness in Machine Learning
Solon Barocas, Moritz Hardt
谷歌大腦的研究科學(xué)家Samy Bengio是這屆大會的程序委員會主席（Program Chair），同時也將參加元學(xué)習(xí)的研討會（Workshop on Meta-Learning）以及組織“敵對攻擊和防御”（Adversarial Attacks and Defences）的比賽。
Workshop on Meta-Learning
Panelists include: Samy Bengio
Competitions
Adversarial Attacks and Defences
Organizers include: Alexey Kurakin, Ian Goodfellow, Samy Bengio
Ian Goodfellow是本屆大會的領(lǐng)域主席。由他組織了“機(jī)器欺騙”（Machine Deception）的研討會，此外他還將在一系列研討會中做特邀報告/keynote 報告：
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，他還將和Samy Bengio、Alexey Kurakin等人共同組織“對抗攻擊防御”（Adversarial Attacks and Defences）的比賽，這個比賽也是Ian Goodfellow所力推的。
作為國內(nèi)諸多研究學(xué)子心目中的女神，李飛飛在NIPS上的活動相比于前面兩位大神則顯得有點少，她將出現(xiàn)在8日的這個研討會中：
Machine Learning for Health (ML4H)
Organizers include: Jasper Snoek, Alex Wiltschko
Keynote: Fei-Fei Li
記著，中午12點整開講。
Hinton在本次大會上甚至比李飛飛還要低調(diào)——只有入選的一篇論文，就是那個火爆一時的《Dynamic Routing Between Capsules》。然而，這篇論文甚至連oral都不是，只有一個5分鐘的spotlight。
Dynamic Routing Between Capsules
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton
注意了，5日下午4: 20-6: 00，Hall A。為了聆聽膠囊理論，估計這個會廳會擠爆頭！
去，要盡早！
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