{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/optimal-transport-tools-ott-a-jax-toolbox-for","title":"Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein","arxiv_id":"2201.12324","date":"2022-01-28","proceeding":null,"authors":["Marco Cuturi","Laetitia Meng-Papaxanthos","Yingtao Tian","Charlotte Bunne","Geoff Davis","Olivier Teboul"],"abstract":"Optimal transport tools (OTT-JAX) is a Python toolbox that can solve optimal transport problems between point clouds and histograms. The toolbox builds on various JAX features, such as automatic and custom reverse mode differentiation, vectorization, just-in-time compilation and accelerators support. The toolbox covers elementary computations, such as the resolution of the regularized OT problem, and more advanced extensions, such as barycenters, Gromov-Wasserstein, low-rank solvers, estimation of convex maps, differentiable generalizations of quantiles and ranks, and approximate OT between Gaussian mixtures. The toolbox code is available at \\texttt{https://github.com/ott-jax/ott}","url_abs":"https://arxiv.org/abs/2201.12324v1","url_pdf":"https://arxiv.org/pdf/2201.12324v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"optimal-transport-tools-ott-a-jax-toolbox-for","repo_url":"https://github.com/ott-jax/ott","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2201.12324","atlas_url":"https://app.syntology.ai/?focus=2201.12324","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}