Awesome resources on normalizing flows.
-
Updated
Apr 12, 2024 - Python
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
Manifold-learning flows (ℳ-flows)
Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"
Code for reproducing Flow ++ experiments
Pytorch implementation of Block Neural Autoregressive Flow
Implementation of normalizing flows in TensorFlow 2 including a small tutorial.
Real NVP PyTorch a Minimal Working Example | Normalizing 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
Probabilistic Learning for mlr3
Likelihood-free AMortized Posterior Estimation with PyTorch
Discrete Normalizing Flows implemented in PyTorch
Distance-based Analysis of DAta-manifolds in python
Neural Relation Understanding: neural cardinality estimators for tabular data
Regularized Neural ODEs (RNODE)
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."