Published April 1, 2021 | Version v1
Journal article

POT : Python Optimal Transport

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Description

Optimal transport has recently been reintroduced to the machine learning community thanks in part to novel efficient optimization procedures allowing for medium to large scale applications. We propose a Python toolbox that implements several key optimal transport ideas for the machine learning community. The toolbox contains implementations of a number of founding works of OT for machine learning such as Sinkhorn algorithm and Wasserstein barycenters, but also provides generic solvers that can be used for conducting novel fundamental research. This toolbox, named POT for Python Optimal Transport, is open source with an MIT license.

Abstract

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Additional details

Identifiers

URL
https://hal.archives-ouvertes.fr/hal-03264013
URN
urn:oai:HAL:hal-03264013v1

Origin repository

Origin repository
UNICA