News

POT (The Python Optimal Transport toolbox) is a fantastic library for coding optimal transport problems in Python. The associated technical publication has been published in the Software Track of JMLR. Please check [the POT website].

Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions. Yet, its original formulation relies on the existence of a cost function between the samples of the two distributions, which makes it impractical for comparing data distributions supported on different topological spaces.

One paper accepted at ACCV 2020 on Contextual Semantic Interpretability, with Diego Marcos, Ruth Fong, Sylvain Lobry, Rémi Flamary and Devis Tuia. Paper abstract: Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability.

Our in-depth paper on the Fused-Gromov Wasserstein is published in a special issue of MDPI Algorithms on graphs. And it made the cover ! Check the link to the special issue

The paper ‘Learning with minibatch Wasserstein : asymptotic and gradient properties’ from my Phd student Kilian Fatras is featured in the accepted of AISTATS 2020 (joint work with Younes Zine, Rémi Flamary and Rémi Gribonval).

The paper ‘Skeletal mesh animation driven by few positional constraints ’ from my former PhD student Thibaut LeNaour was selected as Best paper award for the conference CASA 2019. Check the link to the paper.