3 papers
stat.ML2026
Sampling from multi-modal distributions on Riemannian manifolds with training-free stochastic interpolants
Alain Durmus, Maxence Noble, Thibaut Pellerin
In this paper, we propose a general methodology for sampling from un-normalized densities defined on Riemannian manifolds, with a particular focus on multi-modal targets that remai…
eess.IV2026
Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models
Maxence Noble, Gonzalo Iñaki Quintana, Benjamin Aubin +1
Diffusion-based image super-resolution (SR) has recently attracted significant attention by leveraging the expressive power of large pre-trained text-to-image diffusion models (DMs…
stat.ML2025
Improving the evaluation of samplers on multi-modal targets
Louis Grenioux, Maxence Noble, Marylou Gabrié
Addressing multi-modality constitutes one of the major challenges of sampling. In this reflection paper, we advocate for a more systematic evaluation of samplers towards two source…