Sampled Gromov Wasserstein

DOI10.1007/s10994-021-06035-1OpenAlexW3184455115MaRDI QIDQ2071351

Tanguy Kerdoncuff, Marc Sebban, Rémi Emonet

Publication date: 28 January 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-021-06035-1

zbMATH Keywords

optimal transportconvergence guaranteesGromov Wasserstein

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

On Assignment Problems Related to Gromov–Wasserstein Distances on the Real Line ⋮ Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification

Uses Software

• Wasserstein GAN

Cites Work

• Unnamed Item
• Unnamed Item
• A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching
• Gromov-Wasserstein distances and the metric approach to object matching
• Local Matching Indicators for Transport Problems with Concave Costs
• Assignment Problems and the Location of Economic Activities
• Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
• The Gromov–Wasserstein distance between networks and stable network invariants
• The Dyck bound in the concave 1-dimensional random assignment model
• Optimal Transport

This page was built for publication: Sampled Gromov Wasserstein