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Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario
Florentin Coeurdoux, Grégoire Ferré, Jean-Philippe Bouchaud
Empirical studies of trained models often report a transient regime in which signal is detectable in a finite gradient descent time window before overfitting dominates. We provide…
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…
MGD: Moment Guided Diffusion for Maximum Entropy Generation
Etienne Lempereur, Nathanaël Cuvelle--Magar, Florentin Coeurdoux +2
Generating samples from limited information is a fundamental problem across scientific domains. Classical maximum entropy methods provide principled uncertainty quantification from…
Normalizing flow sampling with Langevin dynamics in the latent space
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
Normalizing flows (NF) use a continuous generator to map a simple latent (e.g. Gaussian) distribution, towards an empirical target distribution associated with a training data set.…
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution. The algorithm based…
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e.g., images) and their failing to det…