Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"

Lightweight MCMC sampling for PyTorch Models aka My Corona Project

An elegant adaptive importance sampling algorithms for simulations of multi-modal distributions (NeurIPS'20)

Samplers from the paper "Stochastic Gradient MCMC with Repulsive Forces"

SGLD and cSGLD as a PyTorch Optimizer

PyTorch wrapper for Deep Density Estimation Models