Paper list of VI,BNN,etc…
01.A Practical Bayesian Framework for Backpropagation Networks
02.Bayesian Learning for Neural Network
03.Keeping Neural Networks Simple by Minimmizing the Description Length of the Weights
04.Practical Variational Inference for Neural Networks
05.Ensemble Learning in Bayesian Neural Networks
06.Weight Uncertainty in Neural Networks
07.Expectation Propagation for Approximate Bayesian Inference(2001)
08.Probabilistic Backpropagation for Scalable Learning for Bayesian Neural Networks
09.Priors For Infinte Networks (1994)
10.Comuting with Infinite Networks (1997)
11.Deep Neural Networks as Gaussian Processes
12.Representing Inferential Uncertainty in Deep Neural Networks through Sampling (2017)
13.Bayesian Uncertainty Estimation for Batch Normalized Deep Networks (2018)
13.Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
14.Simple and Scalable Predictive Uncertainty (2017)
15.Fast Dropout Training (2013)
16.Variational Dropout and Local Reparameterization Trick (2015)
17.Dropout as a Bayesian Approximation_Representing Model Uncertainty in Deep Learning (2016)
18.Variational Dropout Sparsifies Deep Neural Networks (2017)
19.Relevance Vector Machine Explained (2010)
20.Uncertainty in Deep Learning (2016)
21.Variational Inference using Implicit Distributions (2017)
22.Semi-Implicit Variational Inference (2018)
23.Unbiased Implicit Variational Inference (2019)
24.A Contrastive Divergence for Combining Variational Inference and MCMC (2019)
25.Non-linear Independent Components Estimation (NICE) (2014)
26.Variational Inference with Normalizing Flows (2016)
27.Density Estimation using Real NVP (2017)
28.Glow_Generative Flow with Invertible 1x1 Convolutions (2018)
29.What Uncertainties Do We Need in Bayesian Deep Learning(2017)
30.Uncertainty quantification using Bayesian neural networks in classification_Application to ischemic stroke lesion segmentation (2018)
31.Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference
32.MADE_Masked Autoencoder for Distribution Estimation (2015)
33.Improved Variational Inference with Inverse Autoregressive Flow (2016)
34.Masked Autoregressive Flow for Density Estimation (2017)
( paper 35 ~ 47 : Basics of Variational Inference )
35.Nonparametric Variational Inference(2012)
36.Variational Bayesian Inference with Stochastic Search(2012)
37.Black Box Variational Inference(2013)
38.Variational Inference in Nonconjugate Models(2013)
39.Auto-Encoding Variational Bayes(2014)
40.Doubly Stochastic Variational Bayes for non-Conjugate Inference(2014)
41.Neural Variational Inference and Learning in Belief Networks (2014)
42.Smoothed Gradients for Stochastic Variational Inference (2014)
43.Structured Stochastic Variational Inference (2014)
44.Stochastic Backpropagation and Approximate Inference in Deep Generative Models(2014)
45.Copula variational inference(2015)
46.Hierarchical Variational Models(2015)
47.Markov Chain Monte Carlo and Variational Inference ; Briding the Gap
48.A Stochastic Decoder for Neural Machine Translation (2018)
49.Gaussian Processes for Big Data (2013)
50.Adversarial Autoencoders (2016)
51.Practical Deep Learning with Bayesian Principles (2019)
52.Autoencoding Variational Inference for Topic Models (2017)
53.Topic Modeling in Embedding Spaces (2019)
54.Neural Variational Inference for Text Processing (2016)
55.A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference (2019)
56.A Theoretically Grounded Application of Dropout in Recurrent Neural Networks (2016)
57.Variational Inference ; A Review for Statisticians
58.Advances in Variational Inference
59.f-VAEs ; Improve VAEs with Conditional Flows
60.Normalizing Flows ; An Introduction and Review of Current Methods (2020)
61.Normalizing Flows for Probabilistic Modeling and Inference (2019)