7 papers
Sinkhorn Normalization of Diffusion Kernels
Nathan Kessler, Robin Magnet, Jean Feydy
Smoothing a signal based on local neighborhoods is a core operation in machine learning and geometry processing. On well-structured domains such as vector spaces and manifolds, the…
Multicellular simulations with shape and volume constraints using optimal transport
Antoine Diez, Jean Feydy
Many living and physical systems such as cell aggregates, tissues or bacterial colonies behave as unconventional systems of particles that are strongly constrained by volume exclus…
Differentiable latent structure discovery for interpretable forecasting in clinical time series
Ivan Lerner, Jean Feydy, Alexandre Kalimouttou +2
Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, order…
Gromov-Wasserstein at Scale, Beyond Squared Norms
Guillaume Houry, Jean Feydy, François-Xavier Vialard
A fundamental challenge in data science is to match disparate point sets with each other. While optimal transport efficiently minimizes point displacements under a bijectivity cons…
Untangling Vascular Trees for Surgery and Interventional Radiology
Guillaume Houry, Tom Boeken, Stéphanie Allassonnière +1
The diffusion of minimally invasive, endovascular interventions motivates the development of visualization methods for complex vascular networks. We propose a planar representation…
Fast Large Deformation Matching with the Energy Distance Kernel
Siwan Boufadene, François-Xavier Vialard, Jean Feydy
We propose an efficient framework for point cloud and measure registration using bi-Lipschitz homeomorphisms, achieving O(n log n) complexity, where n is the number of points. By l…