Distance-based Analysis of DAta-manifolds in python
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Updated
Aug 8, 2024 - Python
Distance-based Analysis of DAta-manifolds in python
Likelihood-free AMortized Posterior Estimation with PyTorch
Normalizing flows in PyTorch
Parameterizing n-dimensional density fields using denoising diffusion probabilistic models
Factorized kernel density (code originally written by Riccardo "Jack" Lucchetti)
PyTorch implementation of normalizing flow models
Modern normalizing flows in Python. Simple to use and easily extensible.
My personal implementation of several unsupervised learning algorithms.
Awesome resources on normalizing flows.
[NeurIPS 2023] Training Energy-Based Normalizing Flow with Score-Matching Objectives
Official code of the ICML24 paper: "Winner-takes-all learners are geometry-aware conditional density estimators"
Official PyTorch code for UAI 2024 paper "ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding"
Estimators for the entropy and other information theoretic quantities of continuous distributions
Open source implementation to the paper "IKFlow: Generating Diverse Inverse Kinematics Solutions"
This is an unofficial implementation of the KRnet with Pytorch, which Tensorflow originally implemented.
Roundtrip: density estimation with deep generative neural networks
conditional density estimation with neural networks
Reduce a large and high-dimensional dataset by downselecting data uniformly in phase space
Surjection layers for density estimation with normalizing flows
Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data
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