34 citations · 65 across the 7 of their papers we have counts for
8 papers · 1 filter
Template based Graph Neural Network with Optimal Transport Distances
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli +2
Current Graph Neural Networks (GNN) architectures generally rely on two important components: node features embedding through message passing, and aggregation with a specialized fo…
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
Kilian Fatras, Thibault Séjourné, Nicolas Courty +1
Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions. Yet their algorithmic complexit…
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…
Online Graph Dictionary Learning
Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary +2
Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements. Yet, this analysis is not amenable in the context…
Representation Transfer by Optimal Transport
Xuhong Li, Yves Grandvalet, Rémi Flamary +2
Learning generic representations with deep networks requires massive training samples and significant computer resources. To learn a new specific task, an important issue is to tra…
Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN
Jean-Christophe Burnel, Kilian Fatras, Nicolas Courty
Adversarial examples are a hot topic due to their abilities to fool a classifier's prediction. There are two strategies to create such examples, one uses the attacked classifier's…