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)