4 papers · 1 filter
Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models
Hamza Cherkaoui, Hélène Halconruy, Antonio Ocello
Diffusion and flow-matching models may struggle to accurately represent distributional tails, especially for heavy-tailed data. Existing approaches often use heavy-tailed noise or…
Theoretical Guarantees for SMC-Guided Diffusion Sampling
Stanislas Strasman, Gabriel Victorino Cardoso, Sylvain Le Corff +2
Post-hoc conditioning of pretrained diffusion models can be addressed using Sequential Monte Carlo (SMC) methods. By evolving an interacting particle system, SMC-guided diffusion s…
Do Heavy Tails Help Diffusion? On the Subtle Trade-off Between Initialization and Training
Hamza Cherkaoui, Hélène Halconruy, Antonio Ocello
Recent works have proposed incorporating heavy-tailed (HT) noise into diffusion- and flow-based generative models, with the goals of better recovering the tails of target distribut…
On Forgetting and Stability of Score-based Generative models
Stanislas Strasman, Gabriel Cardoso, Sylvain Le Corff +2
Understanding the stability and long-time behavior of generative models is a fundamental problem in modern machine learning. This paper provides quantitative bounds on the sampling…