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A new method for determining Wasserstein 1 optimal transport maps from
  Kantorovich potentials, with deep learning applications

A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applications

2 November 2022
Tristan Milne
Étienne Bilocq
A. Nachman
    OT
ArXivPDFHTML

Papers citing "A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applications"

4 / 4 papers shown
Title
Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction
Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction
Yanshuo Chen
Zhengmian Hu
Wei Chen
Heng Huang
OT
47
1
0
01 Nov 2024
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical
  Points and Primal-Dual Optimisation
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Zakhar Shumaylov
Jeremy Budd
Subhadip Mukherjee
Carola-Bibiane Schönlieb
23
6
0
01 Feb 2024
The Intrinsic Dimension of Images and Its Impact on Learning
The Intrinsic Dimension of Images and Its Impact on Learning
Phillip E. Pope
Chen Zhu
Ahmed Abdelkader
Micah Goldblum
Tom Goldstein
197
260
0
18 Apr 2021
Wasserstein-2 Generative Networks
Wasserstein-2 Generative Networks
Alexander Korotin
Vage Egiazarian
Arip Asadulaev
Alexander Safin
E. Burnaev
GAN
125
100
0
28 Sep 2019
1