Learning Variational Data Assimilation Models and Solvers

2 code implementations • 25 Jul 2020 • Ronan Fablet, Bertrand Chapron, Lucas. Drumetz, Etienne Memin, Olivier Pannekoucke, Francois Rousseau

Intriguingly, we also show that the variational models issued from the true Lorenz-63 and Lorenz-96 ODE representations may not lead to the best reconstruction performance.

Joint learning of variational representations and solvers for inverse problems with partially-observed data

5 code implementations • 5 Jun 2020 • Ronan Fablet, Lucas. Drumetz, Francois Rousseau

The variational cost and the gradient-based solver are both stated as neural networks using automatic differentiation for the latter.

Image Inpainting Time Series +1

Residual Networks as Geodesic Flows of Diffeomorphisms

no code implementations • 24 May 2018 • Francois Rousseau, Ronan Fablet

This paper addresses the understanding and characterization of residual networks (ResNet), which are among the state-of-the-art deep learning architectures for a variety of supervised learning problems.