Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

Deep probabilistic analysis of single-cell and spatial omics data

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

Tensorflow Implementation of Knowledge-Guided CVAE for dialog generation ACL 2017. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU

An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.

Complex-valued neural networks for pytorch and Variational Dropout for real and complex layers.

High-performance reactive message-passing based Bayesian inference engine

Official Pytorch code for (AAAI 2020) paper "Capsule Routing via Variational Bayes", https://arxiv.org/pdf/1905.11455.pdf

MLSS2019 Tutorial on Bayesian Deep Learning

Single-cell Hierarchical Poisson Factorization

Dimensionality reduction of spikes trains

Clustering with variational Bayes and population Monte Carlo

Model for learning document embeddings along with their uncertainties

Variational Bayes linear and logistic regression

A simple library to run variational inference on Stan models.

Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)

Scalable sparse Bayesian learning for large CS recovery problems

A study on the following problems: what the memorization problem is in meta-learning; why memorization problem happens; and how we can prevent it. (ICLR 2020)

A toolbox for inference of mixture models

This repository is for sharing the scripts of EM algorithm and variational bayes.