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A curated collection of papers, code, and resources for domain adaptation in machine learning.
A comprehensive Python library for solving optimal transport problems with solvers for linear, entropic, Gromov-Wasserstein, and unbalanced OT, plus machine learning applications.
A JAX-powered library for solving large-scale optimal transport problems, including matching, barycenters, and neural approximations.
A Python toolbox for solving optimal transport problems with JAX-powered computational efficiency.
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