ICML2018论文公布!一文了解机器学习最新热议论文和研究热点

【导读】ICML 2018昨天公布了会议接受论文,各家组织机构和研究大牛们在Twitter上纷纷报喜,放出接受论文,恭喜!有Google Brain、DeepMind、Facebook、微软和各大高校等。专知整理了Twitter上的关注度比较热的一些论文,供大家了解,最新关于机器学习的一些热门研究方向!

  1.  Differentiable Dynamic Programming for Structured Prediction and Attention

最热的就是这篇Arthur Mensch‏,来自法国Inria Parietal,scikit-learn 作者之一,关于结构性预测与注意力中的可微分动态编程。

作者重点指出:Sparsity and backprop in CRF-like inference layers using max-smoothing, application in text + time series (NER, NMT, DTW)。


Twitter上截止到现在 600赞。

论文网址:

http://www.zhuanzhi.ai/document/34c4176a60e002b524b56b5114db0e78

这位评价甚高! one of the most innovative deep learning papers!

欢迎大家阅读!



2. WaveRNN、Parralel WaveNet 

来自DeepMind的两篇论文关于语音合成!

WaveRNN: arxiv.org/abs/1802.08435

Parallel WaveNet: arxiv.org/abs/1711.10433

WaveNet早已名声卓著,比原来快千倍,语音更自然,已经用在Google自家产品Google Assistant 里~



3. GAN性能表现分析

来自谷歌大脑GoodFellow团队,Is Generator Conditioning Causally Related to GAN Performance? find: 1. Spectrum of G's in/out Jacobian predicts Inception Score. 2. Intervening to change spectrum affects scores a lot


论文链接:https://t.co/cXQDEE2Uee


4.优化方法 Adam分析

Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients

论文地址:https://arxiv.org/abs/1705.07774



5. 图像转换器

论文地址:https://arxiv.org/abs/1802.05751


其他论文列表:



论文地址:Bayesian Quadrature for Multiple Related             

                  Integrals https://arxiv.org/abs/1801.04153                  

                  Stein Points

                  https://arxiv.org/abs/1803.10161




Active Learning with Logged Data

https://arxiv.org/abs/1802.09069

Analyzing the Robustness of Nearest Neighbors to Adversarial Examples

https://arxiv.org/abs/1706.03922



Hierarchical Imitation and Reinforcement Learning

https://arxiv.org/abs/1803.00590


Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model

https://arxiv.org/abs/1802.04551



Detecting and Correcting for Label Shift with Black Box Predictors

https://arxiv.org/abs/1802.03916



Yes, but Did It Work?: Evaluating Variational Inference

https://arxiv.org/abs/1802.02538



MAGAN: Aligning Biological Manifolds

https://arxiv.org/abs/1803.00385



Does Distributionally Robust Supervised Learning Give Robust Classifiers?

https://arxiv.org/abs/1611.02041


Knowledge Transfer with Jacobian Matching

https://arxiv.org/abs/1803.00443


Kronecker Recurrent Units

https://arxiv.org/abs/1705.10142


Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors

https://arxiv.org/abs/1712.09376


The Manifold Assumption and Defenses Against Adversarial Perturbations

https://arxiv.org/abs/1711.08001


Overcoming catastrophic forgetting with hard attention to the task

https://arxiv.org/abs/1801.01423


On the Opportunities and Pitfalls of Nesting Monte Carlo Estimators

https://arxiv.org/abs/1709.06181


Tighter Variational Bounds are Not Necessarily Better

https://arxiv.org/abs/1802.04537


LaVAN: Localized and Visible Adversarial Noise

https://arxiv.org/abs/1801.02608


Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

https://arxiv.org/abs/1711.09576


Geometry Score: A Method For Comparing Generative Adversarial Networks

https://arxiv.org/abs/1802.02664


更多最新论文,请关注专知!获取更多AI知识信息资料,加入专知AI会员计划,学习、讨论、交流、合作:

点击上面图片加入会员


-END-

专 · 知

请PC登录www.zhuanzhi.ai或者点击阅读原文,注册登录专知,获取更多AI知识资料

请加专知小助手微信(扫一扫如下二维码添加),加入专知主题群(请备注主题类型:AI、NLP、CV、 KG等)交流~

关注专知公众号,获取人工智能的专业知识!

点击“阅读原文”,使用专知

展开全文
Top
微信扫码咨询专知VIP会员