不僅是白金贊助商，谷歌更是ICML 2017 的重量級(jí)參與者（附59篇收錄論文下載） | ICML 2017

本文作者：楊曉凡

2017-08-07 15:30

專題：ICML 2017

導(dǎo)語(yǔ)：ICML 2017七分之一的收錄論文都在這里了

雷鋒網(wǎng) AI 科技評(píng)論按：機(jī)器學(xué)習(xí)領(lǐng)域頂級(jí)會(huì)議 ICML 2017 已經(jīng)開始了，雷鋒網(wǎng)記者會(huì)帶來(lái)全方位的大會(huì)報(bào)道。

在之前的文章中，雷鋒網(wǎng) AI 科技評(píng)論就介紹過(guò)434篇 ICML 收錄論文中有多達(dá)44篇都出現(xiàn)了谷歌的名字，谷歌的在機(jī)器學(xué)習(xí)領(lǐng)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(gè) workshop 中。根據(jù)我們前兩天的報(bào)道，署名DeepMind的收錄論文也有25篇之多。那么來(lái)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(gè)數(shù)字簡(jiǎn)直大到讓人有點(diǎn)害怕了。

谷歌在文中說(shuō)，機(jī)器學(xué)習(xí)是谷歌的重點(diǎn)戰(zhàn)略之一，他們有非?；钴S的研究小組在領(lǐng)域內(nèi)的各個(gè)方面進(jìn)行研究，包括深度學(xué)習(xí)和更多的傳統(tǒng)算法，理論和應(yīng)用探索并重。谷歌的研究人員們運(yùn)用可拓展的工具和架構(gòu)，構(gòu)建出各種各樣的機(jī)器學(xué)習(xí)系統(tǒng)供他們解決語(yǔ)言、語(yǔ)音、翻譯、音樂(lè)、視覺(jué)處理等等方面艱深的科學(xué)和工程問(wèn)題。

作為機(jī)器學(xué)習(xí)領(lǐng)域的帶頭人之一，谷歌不僅是今年 ICML 2017的白金贊助商，也實(shí)實(shí)在在做出了許多研究成果（體現(xiàn)為42篇接收論文），此次參加會(huì)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(gè)機(jī)器學(xué)習(xí)大家庭有更多的溝通和協(xié)作。

除了論文和workshop，谷歌的研究人員們還會(huì)對(duì)一些新的研究成果做講解和展示，比如介紹 Facets 背后的技術(shù)、音頻生成神經(jīng)網(wǎng)絡(luò) Nsynth，還會(huì)有一個(gè)關(guān)于谷歌大腦培訓(xùn)生計(jì)劃的問(wèn)答活動(dòng)。

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(guān)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 ?p 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論文，workshop時(shí)間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論文，workshop時(shí)間8月11日
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models
Learning to Generate Natural Language Workshop論文，workshop時(shí)間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
谷歌與DeepMind合作論文
Neural Message Passing for Quantum Chemistry
谷歌與DeepMind合作論文
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論文，workshop時(shí)間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