雷鋒網 AI 科技評論按：機器學習領域頂級會議 ICML 2017 已經開始了，雷鋒網(公眾號：雷鋒網)記者會帶來全方位的大會報導。
在之前的文章中，雷鋒網 AI 科技評論就介紹過434篇 ICML 收錄論文中有多達44篇都出現了谷歌的名字，谷歌的在機器學習領域的投入與成果之多可見一斑。今天谷歌也正式給出了自己的收錄論文名單，署名的谷歌的就有42篇，其中有4篇是在幾個 workshop 中。根據我們前兩天的報導，署名DeepMind的收錄論文也有25篇之多。那麼來自谷歌的全部論文就有65篇（其中2篇是谷歌和DeepMind合作完成的），大約是 ICML 2017 全部收錄論文的七分之一。這個數字簡直大到讓人有點害怕了。
谷歌在文中說，機器學習是谷歌的重點戰略之一，他們有非常活躍的研究小組在領域內的各個方面進行研究，包括深度學習和更多的傳統算法，理論和應用探索並重。谷歌的研究人員們運用可拓展的工具和架構，構建出各種各樣的機器學習系統供他們解決語言、語音、翻譯、音樂、視覺處理等等方面艱深的科學和工程問題。
作為機器學習領域的帶頭人之一，谷歌不僅是今年 ICML 2017的白金贊助商，也實實在在做出了許多研究成果（體現為42篇接收論文），此次參加會議展示論文、組織workshop的研究人員也有130人之多，熱切地希望跟整個機器學習大家庭有更多的溝通和協作。
除了論文和workshop，谷歌的研究人員們還會對一些新的研究成果做講解和展示，比如介紹 Facets 背後的技術、音頻生成神經網絡 Nsynth，還會有一個關於谷歌大腦培訓生計劃的問答活動。
谷歌在文中給出了自己的42篇論文列表，感興趣的讀者可以具體關注一下，打包下載地址見文末
A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions
Accelerating Eulerian Fluid Simulation With Convolutional Networks
AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP
Algorithms for ℓp Low-Rank Approximation
Axiomatic Attribution for Deep Networks
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Canopy Fast Sampling with Cover Trees
Conditional Image Synthesis with Auxiliary Classifier GANs
Consistent k-Clustering
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs
Density Level Set Estimation on Manifolds with DBSCAN
Device Placement Optimization with Reinforcement Learning
Differentiable Programs with Neural Libraries
Distributed Mean Estimation with Limited Communication
Filtering Variational Objectives
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models
Geometry of Neural Network Loss Surfaces via Random Matrix Theory
Gradient Boosted Decision Trees for High Dimensional Sparse Output
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
Large-Scale Evolution of Image Classifiers
Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data
Learned Optimizers that Scale and Generalize
Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo
Learning to Generate Long-term Future via Hierarchical Prediction
Maximum Selection and Ranking under Noisy Comparisons
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
Neural Message Passing for Quantum Chemistry
Neural Optimizer Search with Reinforcement Learning
On the Expressive Power of Deep Neural Networks
Online and Linear-Time Attention by Enforcing Monotonic Alignments
Probabilistic Submodular Maximization in Sub-Linear Time
REBAR: Low-variance unbiased gradient estimates for discrete latent variable models
Robust Adversarial Reinforcement Learning
RobustFill: Neural Program Learning under Noisy IO
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
Sharp Minima Can Generalize For Deep Nets
Stochastic Generative Hashing
Tight Bounds for Approximate Carathéodory and Beyond
Uniform Convergence Rates for Kernel Density Estimation
Variational Boosting: Iteratively Refining Posterior Approximations
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
via Google Research Blog
42篇谷歌署名論文+17篇DeepMind署名演講論文打包下載連結:
http://pan.baidu.com/s/1jIFYZqu 密碼: t74m
雷鋒網 AI 科技評論記者也已經在 ICML現場參與大會活動，更多報導請繼續關注。
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