Paper Info
Title
Pot: Python Optimal Transport
Abstract
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.
Year
DOI
Venue
2021
v22/20-451.html
JOURNAL OF MACHINE LEARNING RESEARCH
Keywords
DocType
Volume
Optimal transport, divergence, optimization, domain adaptation
Journal
22
Issue
ISSN
Citations 
78
1532-4435
0
PageRank 
References 
Authors
0.34
0
22
Name
Order
Citations
PageRank
Rémi Flamary100.34
Nicolas Courty242044.55
Alexandre Gramfort300.34
Mokhtar Z. Alaya402.37
Aurélie Boisbunon500.68
Stanislas Chambon600.34
Laetitia Chapel700.68
Adrien Corenflos800.34
Kilian Fatras900.34
Nemo Fournier1000.34
Léo Gautheron1100.34
Nathalie T.H. Gayraud1200.34
Hicham Janati1300.34
Alain Rakotomamonjy1440.79
Ievgen Redko15186.84
Antoine Rolet16272.54
Antony Schutz1700.34
Vivien Seguy18152.78
Danica J. Sutherland1900.34
Romain Tavenard2000.34
Alexander Tong2100.34
Titouan Vayer2200.34