Awesome resources on normalizing flows.
-
Updated
Jul 1, 2024 - Python
Awesome resources on normalizing flows.
Normalizing flows in PyTorch
PyTorch implementation of normalizing flow models
PyTorch implementations of algorithms for density estimation
Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
Implementation of normalizing flows in TensorFlow 2 including a small tutorial.
Pytorch implementation of Block Neural Autoregressive Flow
Neural Relation Understanding: neural cardinality estimators for tabular data
Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead
Libraries to analyze numerical simulations (python3)
Real NVP PyTorch a Minimal Working Example | Normalizing Flow
Code for reproducing Flow ++ experiments
[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"
Manifold-learning flows (ℳ-flows)
Estimators for the entropy and other information theoretic quantities of continuous distributions
Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data
Normalizing flows in PyTorch
Code for reproducing results in the sliced score matching paper (UAI 2019)
Probabilistic Learning for mlr3
Distance-based Analysis of DAta-manifolds in python
Add a description, image, and links to the density-estimation topic page so that developers can more easily learn about it.
To associate your repository with the density-estimation topic, visit your repo's landing page and select "manage topics."