Bayesian neural network papers
Orginal
What I like the most:
Towards better training algorithms

Keeping Neural Networks Simple by Minimizing the Description Length of the Weights

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

Assumed Density Filtering Methods for Learning Bayesian Neural Networks

Dropout Inference in Bayesian Neural Networks with Alphadivergences
Towards more expressive posteriors

Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors

Learning Structured Weight Uncertainty in Bayesian Neural Networks

Multiplicative Normalizing Flows for Variational Bayesian Neural Networks

Fast and Scalable Bayesian Deep Learning by WeightPerturbation in Adam

Adversarial Distillation of Bayesian Neural Network Posteriors

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Towards better models (Structure + Prior)

Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Variance Networks: When Expectation Does Not Meet Your Expectations

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks

Understanding Priors in Bayesian Neural Networks at the Unit Level

Function Space Particle Optimization for Bayesian Neural Networks
Connecting Bayesian neural networks with Gaussian Processes
Towards applications

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

Model Selection in Bayesian Neural Networks via Horseshoe Priors

Learning Structural Weight Uncertainty for Sequential DecisionMaking

Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting

LossCalibrated Approximate Inference in Bayesian Neural Networks