AI Chat AI Image Generator AI Video Text to Speech

Optimal transport with some directed distances

05/12/2021
∙
by   Wolfgang Stummer, et al.
∙
0
∙

We present a toolkit of directed distances between quantile functions. By employing this, we solve some new optimal transport (OT) problems which e.g. considerably flexibilize some prominent OTs expressed through Wasserstein distances.

READ FULL TEXT

page 1

page 2

page 3

page 4

Related Research

research
∙ 09/13/2023

Optimal transport distances for directed, weighted graphs: a case study with cell-cell communication networks

Comparing graphs by means of optimal transport has recently gained signi...
0 James S. Nagai, et al. ∙
research
∙ 03/24/2023

Optimal transport and Wasserstein distances for causal models

In this paper we introduce a variant of optimal transport adapted to the...
0 Stephan Eckstein, et al. ∙
research
∙ 02/08/2021

Optimal Transport in the Face of Noisy Data

Optimal transport distances are popular and theoretically well understoo...
0 Bart P. G. Van Parys, et al. ∙
research
∙ 12/22/2021

On a linear Gromov-Wasserstein distance

Gromov-Wasserstein distances are generalization of Wasserstein distances...
0 Florian Beier, et al. ∙
research
∙ 06/25/2018

Towards Optimal Transport with Global Invariances

Many problems in machine learning involve calculating correspondences be...
0 David Alvarez-Melis, et al. ∙
research
∙ 04/05/2023

Distance maps between Japanese kanji characters based on hierarchical optimal transport

We introduce a general framework for assigning distances between kanji b...
0 Dominic Schuhmacher, et al. ∙
research
∙ 01/28/2022

Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein

Optimal transport tools (OTT-JAX) is a Python toolbox that can solve opt...
4 Marco Cuturi, et al. ∙

Please sign up or login with your details

Forgot password? Click here to reset

Out of credits

Refill your membership to continue using DeepAI

Stripe
Paypal