Learning to Transport with Neural Networks

Andrea Schioppa

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arXiv · 1908.01394

Learning to Transport with Neural Networks

Andrea Schioppa

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Abstract

We compare several approaches to learn an Optimal Map, represented as a neural network, between probability distributions. The approaches fall into two categories: ``Heuristics'' and approaches with a more sound mathematical justification, motivated by the dual of the Kantorovitch problem. Among the algorithms we consider a novel approach involving dynamic flows and reductions of Optimal Transport to supervised learning.

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Andrea Schioppa. 2019-08-04. Learning to Transport with Neural Networks. https://arxiv.org/abs/1908.01394

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