A Collection of Variational Autoencoders (VAE) in PyTorch.

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

Bayesian Deep Learning: A Survey

Easy generative modeling in PyTorch

DGMs for NLP. A roadmap.

I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

Code for the paper "VAE with a VampPrior", J.M. Tomczak & M. Welling

(FTML 2021) Official implementation of Dynamical VAEs

Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification

Deep and Machine Learning for Microscopy

Training and evaluating a variational autoencoder for pan-cancer gene expression data

Variational Graph Recurrent Neural Networks - PyTorch

[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"

Ladder Variational Autoencoders (LVAE) in PyTorch

This repository tries to provide unsupervised deep learning models with Pytorch

Deep active inference agents using Monte-Carlo methods

Code for the paper "Improving Variational Auto-Encoders using Householder Flow" (https://arxiv.org/abs/1611.09630)

Computer code collated for use with Artificial Intelligence Engines book by JV Stone

(beta-)VAE Tensorflow