1 citations · 1 across the 5 of their papers we have counts for
6 papers
Variational Augmented Invertible Koopman Autoencoder for probabilistic time series forecasting
Anthony Frion, Lucas Drumetz, Guillaume Tochon +3
Neural Koopman autoencoder models have been shown to successfully build a latent embedding with linear dynamics for arbitrary dynamical systems, enabling strong performance in long…
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 through Geometric Properties
David Vigouroux, Lucas Drumetz, Ronan Fablet +1
A prevailing paradigm in modern representation learning is the map-first approach, in which a representation map is learned from reconstruction, embedding, or task objectives. At t…
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…
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…
Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds
Clément Bonet, Lucas Drumetz, Nicolas Courty
While many Machine Learning methods were developed or transposed on Riemannian manifolds to tackle data with known non Euclidean geometry, Optimal Transport (OT) methods on such sp…