Optimal Transport-based Loss Functions for Machine Learning
Keywords:
Geometry, Neural Network, Deep Learning, WGAN, Intelligent ApplicationsSynopsis
This is a Chapter in:
Book:
Smart and Sustainable Applications
Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5
Series:
Chronicle of Computing
Chapter Abstract:
This short paper briefly reports the essential facets of the article (Kamsu-Foguem & al., 2022) presented and discussed as a Journal First paper. The article overviews generative neural networks whose loss functions are based on optimal transport with the Wasserstein distance. This tool of mathematical origin allows interesting automatic learning to be obtained in a reasoning time under Lipschitz constraints. As the proposed studies are based on Wasserstein Generative Adversarial Networks (WGAN), we conclude this report with a short discussion on how WGAN currently supports critical, intelligent applications in our society and nearly all industry sectors.
Keywords:
Geometry, Neural Network, Deep Learning, WGAN, Intelligent Applications
Cite this paper as:
Kamsu-Foguem B., Tiako P.F. & Kounta C.A.K.A. (2024) Optimal Transport-based Loss Functions for Machine Learning. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_10
Presented at:
The 2023 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023
Contact:
Bernard K. Foguem
Bernard.Kamsu-Foguem@enit.fr
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