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variational-autoencoders
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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
[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"
Easy generative modeling in PyTorch
DGMs for NLP. A roadmap.
Repository for Deep Structural Causal Models for Tractable Counterfactual Inference
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
(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
Deep active inference agents using Monte-Carlo methods
This repository tries to provide unsupervised deep learning models with Pytorch
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
