4 papers
Busemann Functions in the Wasserstein Space: Existence, Closed-Forms, and Applications to Slicing
Clément Bonet, Elsa Cazelles, Lucas Drumetz +1
The Busemann function has recently found much interest in a variety of geometric machine learning problems, as it naturally defines projections onto geodesic rays of Riemannian man…
Static and auto-regressive neural emulation of phytoplankton biomass dynamics from physical predictors in the global ocean
Mahima Lakra, Ronan Fablet, Lucas Drumetz +2
Phytoplankton is the basis of marine food webs, driving both ecological processes and global biogeochemical cycles. Despite their ecological and climatic significance, accurately s…
Discovering Data Manifold Geometry via Non-Contracting Flows
David Vigouroux, Lucas Drumetz, Ronan Fablet +1
We introduce an unsupervised approach for constructing a global reference system by learning, in the ambient space, vector fields that span the tangent spaces of an unknown data ma…
Land Surface Temperature Super-Resolution with a Scale-Invariance-Free Neural Approach: Application to MODIS
Romuald Ait-Bachir, Carlos Granero-Belinchon, Aurélie Michel +3
Due to the trade-off between the temporal and spatial resolution of thermal spaceborne sensors, super-resolution methods have been developed to provide fine-scale Land SurfaceTempe…