Differentiable Dynamic Programming for Structured Prediction and Attention
最热的就是这篇第一作者Arthur Mensch，来自法国Inria Parietal，也是scikit-learn 作者之一，论文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: http://arxiv.org/abs/1802.08435
Parallel WaveNet: http://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
原文发布时间为：2018-05-14
本文作者：专知
本文来自云栖社区合作伙伴新智元，了解相关信息可以关注“AI_era”。
