Probabilistic Learning for mlr3
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Updated
Jul 13, 2024 - R
Probabilistic Learning for mlr3
Non-parametric density inference for single-cell analysis.
Parameterizing n-dimensional density fields using denoising diffusion probabilistic models
MeTACAST is a set of selection techniques for particle data in VR environment. Users can select a group of particles with natural and simple 6DOF point/stroke input.
Distance-based Analysis of DAta-manifolds in python
Factorized kernel density (code originally written by Riccardo "Jack" Lucchetti)
PyTorch implementation of normalizing flow models
Sub-package of spatstat containing code for linear networks
Modern normalizing flows in Python. Simple to use and easily extensible.
My personal implementation of several unsupervised learning algorithms.
Awesome resources on normalizing flows.
Normalizing flows in PyTorch
[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"
Implementation of normalizing flows in TensorFlow 2 including a small tutorial.
Official PyTorch code for UAI 2024 paper "ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding"
Density ratio estimation in Julia
A C++ library for physics-informed spatial and functional data analysis over complex domains.
1D Density Estimation with the Haar Wavelet. Application is to separate point sources from a background distribution.
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