48 citations · 56 across the 13 of their papers we have counts for
9 papers · 1 filter
Double Descent and Malign Overfitting in Diffusion Models
Raphaël Urfin, Tony Bonnaire, Giulio Biroli +1
Conventional wisdom in deep learning holds that overparameterization---having more parameters than training samples ---is benign: larger models generalize better and, even w…
Theory of Speciation Transitions in Diffusion Models with General Class Structure
Beatrice Achilli, Marco Benedetti, Giulio Biroli +1
Diffusion Models generate data by reversing a stochastic diffusion process, progressively transforming noise into structured samples drawn from a target distribution. Recent theore…
Why Diffusion Models Don't Memorize: The Role of Implicit Dynamical Regularization in Training
Tony Bonnaire, Raphaël Urfin, Giulio Biroli +1
Diffusion models have achieved remarkable success across a wide range of generative tasks. A key challenge is understanding the mechanisms that prevent their memorization of traini…
Classifier-Free Guidance: From High-Dimensional Analysis to Generalized Guidance Forms
Krunoslav Lehman Pavasovic, Jakob Verbeek, Giulio Biroli +1
Classifier-Free Guidance (CFG) is a widely adopted technique in diffusion and flow-based generative models, enabling high-quality conditional generation. A key theoretical challeng…
Optimizing Noise Schedules of Generative Models in High Dimensionss
Santiago Aranguri, Giulio Biroli, Marc Mezard +1
Recent works have shown that diffusion models can undergo phase transitions, the resolution of which is needed for accurately generating samples. This has motivated the use of diff…
Kernel Density Estimators in Large Dimensions
Giulio Biroli, Marc Mézard
This paper studies Kernel Density Estimation for a high-dimensional distribution . Traditional approaches have focused on the limit of large number of data points and fix…