milenagazdieva/U-NOTBarycenters
PyTorch implementation of "Robust Barycenter Estimation using Semi-unbalanced Neural Optimal Transport" (ICLR 2025)
This project helps researchers and data scientists working with complex data distributions to find a meaningful 'average' representation, called a barycenter. You input several high-dimensional data distributions, potentially containing outliers or class imbalances, and it outputs a single, robust barycenter that accurately reflects the core characteristics of the input data while ignoring noise. It's designed for quantitative researchers, machine learning practitioners, or data analysts dealing with statistical comparisons or generative modeling tasks.
No commits in the last 6 months.
Use this if you need to compute a robust average (barycenter) of multiple data distributions, especially when these distributions might contain noisy data points or have unequal sample sizes.
Not ideal if you are looking for a simple average calculation or if your distributions are low-dimensional and guaranteed to be clean without outliers or imbalances.
Stars
17
Forks
1
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jul 07, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/milenagazdieva/U-NOTBarycenters"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
PythonOT/POT
POT : Python Optimal Transport
recursionpharma/gflownet
GFlowNet library specialized for graph & molecular data
IShengFang/SpectralNormalizationKeras
Spectral Normalization for Keras Dense and Convolution Layers
dfdazac/wassdistance
Approximating Wasserstein distances with PyTorch
iamalexkorotin/NeuralOptimalTransport
PyTorch implementation of "Neural Optimal Transport" (ICLR 2023 Spotlight)