2 papers
cs.LG2021
Learning to Generate Wasserstein Barycenters
Julien Lacombe, Julie Digne, Nicolas Courty +1
Optimal transport is a notoriously difficult problem to solve numerically, with current approaches often remaining intractable for very large scale applications such as those encou…
stat.ML2019
Ground Metric Learning on Graphs
Matthieu Heitz, Nicolas Bonneel, David Coeurjolly +2
Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between observations. Their relevance for real-life applications…