这两天闲来没事，就总结了一下机器学习中与概率相关的内容= =
推荐一些相关文献：
1.生成模型：
(1)VAE：An Introduction to Variational Autoencoders、变分推断与变分自编码器(VAE)
(2)GAN：A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
(3)Flow：Normalizing Flows: An Introduction and Review of Current Methods
(4)Autoregressive Models: introduction for Autoregressive Models
2.divergence：
(1)f-divergence: f-Divergences and Surrogate Loss Functions
(2)Wasserstein distance:【数学】Wasserstein Distance
(3)Stein discrepancy: Stein’s Method for Practical Machine Learning
(4)Fisher divergence: Variational approximations using Fisher divergence
(5)Sliced method for computation acceleration:
a.Fisher divergence: Sliced Score Matching: A Scalable Approach to Density and Score Estimation
b.Wasserstein distance: Generalized Sliced Wasserstein Distances
3.BNN和dropout
(1)BNN:Bayesian Neural Networks
(2)dropout:深度学习中Dropout原理解析
4.贝叶斯元学习：
(1)神经过程：Neural Processes
(2)贝叶斯+MAML：Amortized Bayesian Meta-Learning、Bayesian Model-Agnostic Meta-Learning、Probabilistic Model-Agnostic Meta-Learning
5.高斯过程：Gaussian Processes in Machine Learning
6.表示学习：
(1)信息瓶颈：信息瓶颈理论-基础与应用
(2)解耦表示学习：【Disentangled representation 1】InfoGAN与betaVAE
(3)infoMAX和contrastive learning:Deep InfoMax: Learning good representations through mutual information maximization、A Simple Framework for Contrastive Learning of Visual Representations
7.其他：
(1)信息论：INTRODUCTION TO INFORMATION THEORY
(2)测度论：sola的数学笔记
(3)MCMC：马尔可夫链蒙特卡罗算法（MCMC）
(4)gradient flow：Gradient Flow
(5)贝叶斯优化：贝叶斯优化(Bayesian Optimization)深入理解
(6)其他论文：
Meta Dropout: Learning to Perturb Features for Generalization
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Functional Variational Bayesian Neural Networks
Variational Implicit Processes
The Functional Neural Process
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning