Awesome resources on normalizing flows.

Normalizing flows in PyTorch

PyTorch implementation of normalizing flow models

PyTorch implementations of algorithms for density estimation

Normalizing flows in PyTorch

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

Code for reproducing Flow ++ experiments

Pytorch implementation of Block Neural Autoregressive Flow

Code for reproducing results in the sliced score matching paper (UAI 2019)

Estimators for the entropy and other information theoretic quantities of continuous distributions

Likelihood-free AMortized Posterior Estimation with PyTorch

Distance-based Analysis of DAta-manifolds in python

Neural Relation Understanding: neural cardinality estimators for tabular data

Regularized Neural ODEs (RNODE)

Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data

ML algorithm for real-time classification

Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead

PyTorch implementation of the paper "NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity." (NeurIPS 2020)

Roundtrip: density estimation with deep generative neural networks

Implementation of NeurIPS 20 paper: Latent Template Induction with Gumbel-CRFs