67 citations · 162 across the 5 of their papers we have counts for
7 papers
Probing the Geometry of Diffusion Models with the String Method
Elio Moreau, Florentin Coeurdoux, Grégoire Ferre +1
Understanding the geometry of learned distributions is fundamental to improving and interpreting diffusion models, yet systematic tools for exploring their landscape remain limited…
Large deviations of empirical measures of diffusions in weighted topologies
Grégoire Ferré, Gabriel Stoltz
We consider large deviations of empirical measures of diffusion processes. In a first part, we present conditions to obtain a large deviations principle (LDP) for a precise class o…
Simulating Coulomb gases and log-gases with hybrid Monte Carlo algorithms
Djalil Chafaï, Grégoire Ferré
Coulomb and log-gases are exchangeable singular Boltzmann-Gibbs measures appearing in mathematical physics at many places, in particular in random matrix theory. We explore experim…
Adaptive sampling of large deviations
Grégoire Ferré, Hugo Touchette
We introduce and test an algorithm that adaptively estimates large deviation functions characterizing the fluctuations of additive functionals of Markov processes in the long-time…
Error estimates on ergodic properties of discretized Feynman-Kac semigroups
Grégoire Ferré, Gabriel Stoltz
We consider the numerical analysis of the time discretization of Feynman-Kac semigroups associated with diffusion processes. These semigroups naturally appear in several fields, su…
Learning molecular energies using localized graph kernels
G. Ferré, T. Haut, K. Barros
Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab-initio calculations) and at…
Permutation-invariant distance between atomic configurations
Gregoire Ferre, Jean-Bernard Maillet, Gabriel Stoltz
We present a permutation-invariant distance between atomic configurations, defined through a functional representation of atomic positions. This distance enables to directly compar…