Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Awesome resources on normalizing flows.

Normalizing flows in PyTorch

Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows

An extension of XGBoost to probabilistic modelling

DGMs for NLP. A roadmap.

Normalizing flows in PyTorch

PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech

An extension of LightGBM to probabilistic modelling

Neural Spline Flow, RealNVP, Autoregressive Flow, 1x1Conv in PyTorch.

Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"

Manifold-learning flows (ℳ-flows)

Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)

Network-to-Network Translation with Conditional Invertible Neural Networks

Normalizing-flow enhanced sampling package for probabilistic inference in Jax

Code for reproducing Flow ++ experiments

Pytorch implementation of Block Neural Autoregressive Flow

A Julia framework for invertible neural networks