Sampled Gromov Wasserstein

Optimal Transport (OT) has proven to be a powerful tool to compare probability distributions in machine learning, but dealing with probability measures lying in different spaces remains an open problem. To address this issue, the Gromov Wasserstein distance (GW) only considers intra-distribution pai...

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Veröffentlicht in:	Machine learning 2021-08, Vol.110 (8), p.2151-2186
Hauptverfasser:	Kerdoncuff, Tanguy, Emonet, Rémi, Sebban, Marc
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial Intelligence Complexity Computer Science Control Experiments Machine Learning Mechatronics Natural Language Processing (NLP) Robotics Simulation and Modeling Solvers Special Issue of the ECML PKDD 2021 Journal Track Transportation planning
Online-Zugang:	Volltext
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