385 citations · 1.1k across the 45 of their papers we have counts for
7 papers · 1 filter
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections
Clément Bonet, Laetitia Chapel, Lucas Drumetz +1
It has been shown beneficial for many types of data which present an underlying hierarchical structure to be embedded in hyperbolic spaces. Consequently, many tools of machine lear…
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows
Guillaume Morel, Lucas Drumetz, Simon Benaïchouche +2
Normalizing Flows (NF) are powerful likelihood-based generative models that are able to trade off between expressivity and tractability to model complex densities. A now well estab…
Aligning individual brains with Fused Unbalanced Gromov-Wasserstein
Alexis Thual, Huy Tran, Tatiana Zemskova +4
Individual brains vary in both anatomy and functional organization, even within a given species. Inter-individual variability is a major impediment when trying to draw generalizabl…
Spherical Sliced-Wasserstein
Clément Bonet, Paul Berg, Nicolas Courty +3
Many variants of the Wasserstein distance have been introduced to reduce its original computational burden. In particular the Sliced-Wasserstein distance (SW), which leverages one-…
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 CO-Optimal Transport
Quang Huy Tran, Hicham Janati, Nicolas Courty +4
Optimal transport (OT) compares probability distributions by computing a meaningful alignment between their samples. CO-optimal transport (COOT) takes this comparison further by in…