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

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"

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

Normalizing-flow enhanced sampling package for probabilistic inference in Jax

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.