不仅是白金赞助商，谷歌更是ICML 2017 的重量级参与者（附59篇收录论文下载） | ICML 2017

本文作者：杨晓凡

2017-08-07 15:30

专题：ICML 2017

导语：ICML 2017七分之一的收录论文都在这里了

雷锋网 AI 科技评论按：机器学习领域顶级会议 ICML 2017 已经开始了，雷锋网记者会带来全方位的大会报道。

在之前的文章中，雷锋网 AI 科技评论就介绍过434篇 ICML 收录论文中有多达44篇都出现了谷歌的名字，谷歌的在机器学习领域的投入与成果之多可见一斑。今天谷歌也正式给出了自己的收录论文名单，署名的谷歌的就有42篇，其中有4篇是在几个 workshop 中。根据我们前两天的报道，署名DeepMind的收录论文也有25篇之多。那么来自谷歌的全部论文就有65篇（其中2篇是谷歌和DeepMind合作完成的），大约是 ICML 2017 全部收录论文的七分之一。这个数字简直大到让人有点害怕了。

谷歌在文中说，机器学习是谷歌的重点战略之一，他们有非常活跃的研究小组在领域内的各个方面进行研究，包括深度学习和更多的传统算法，理论和应用探索并重。谷歌的研究人员们运用可拓展的工具和架构，构建出各种各样的机器学习系统供他们解决语言、语音、翻译、音乐、视觉处理等等方面艰深的科学和工程问题。

作为机器学习领域的带头人之一，谷歌不仅是今年 ICML 2017的白金赞助商，也实实在在做出了许多研究成果（体现为42篇接收论文），此次参加会议展示论文、组织workshop的研究人员也有130人之多，热切地希望跟整个机器学习大家庭有更多的沟通和协作。

除了论文和workshop，谷歌的研究人员们还会对一些新的研究成果做讲解和展示，比如介绍 Facets 背后的技术、音频生成神经网络 Nsynth，还会有一个关于谷歌大脑培训生计划的问答活动。

谷歌在文中给出了自己的42篇论文列表，感兴趣的读者可以具体关注一下，打包下载地址见文末

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
Lifelong Learning: A Reinforcement Learning Approach Workshop论文，workshop时间8月10日
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
Deep Structured Prediction Workshop论文，workshop时间8月11日
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models
Learning to Generate Natural Language Workshop论文，workshop时间8月10日
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
谷歌与DeepMind合作论文
Neural Message Passing for Quantum Chemistry
谷歌与DeepMind合作论文
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
Deep Structured Prediction Workshop论文，workshop时间8月11日
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