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

Awesome resources on normalizing flows.

Normalizing flows in PyTorch

An extension of XGBoost to probabilistic modelling

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

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.

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

Normalizing-flow enhanced sampling package for probabilistic inference in Jax

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

Network-to-Network Translation with Conditional Invertible Neural Networks

Code for reproducing Flow ++ experiments

Pytorch implementation of Block Neural Autoregressive Flow

Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)

Likelihood-free AMortized Posterior Estimation with PyTorch

Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.