91 citations · 395 across the 40 of their papers we have counts for
5 papers · 1 filter
Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi +2
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations. This raises limitations in real-world applications like weather predict…
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Thomas Moreau, Mathurin Massias, Alexandre Gramfort +18
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.…
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances
Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy +1
The Sliced-Wasserstein distance (SW) is a computationally efficient and theoretically grounded alternative to the Wasserstein distance. Yet, the literature on its statistical prope…
Diverse Weight Averaging for Out-of-Distribution Generalization
Alexandre Ramé, Matthieu Kirchmeyer, Thibaud Rahier +3
Standard neural networks struggle to generalize under distribution shifts in computer vision. Fortunately, combining multiple networks can consistently improve out-of-distribution…
Generalizing to New Physical Systems via Context-Informed Dynamics Model
Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà +3
Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different…