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

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

(FTML 2021) Official implementation of Dynamical VAEs

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

Deep and Machine Learning for Microscopy

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

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

Variational Graph Recurrent Neural Networks - PyTorch

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