Normalizing flows in PyTorch
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Updated
Nov 19, 2023 - Python
Normalizing flows in PyTorch
Awesome resources on normalizing flows.
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
Real NVP PyTorch a Minimal Working Example | Normalizing Flow
Libraries to analyze numerical simulations (python3)
[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
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
Code for reproducing results in the sliced score matching paper (UAI 2019)
Normalizing flows in PyTorch
Probabilistic Learning for mlr3
Regularized Neural ODEs (RNODE)
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