48 citations · 56 across the 13 of their papers we have counts for
7 papers · 1 filter
First Learn, Then Memorize: The Spectral Bias of Diffusion Models
Raphaël Urfin, Tony Bonnaire, Giulio Biroli +1
Diffusion models trained on a finite dataset first learn to generate novel, high-quality samples and only much later collapse onto their training set. We identify the mechanism beh…
Weighting Schedules Govern What and When Score-Based Generative Models Learn from Multimodal Data
Jérémie Klinger, Raphaël Urfin, Giulio Biroli +1
Score-based generative models generate new samples by integrating a time-dependent drift that carries Gaussian noise onto the target distribution. In practice this drift is modeled…
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…
WaiT for the Signal: Simple Frequency-Aware Flow-Matching
Krunoslav Lehman Pavasovic, Théophane Vallaeys, Stéphane Mallat +4
As image generation models scale to ever higher resolutions, global coherence, local detail, and texture fidelity become critical axes for generation quality. However, standard flo…
The critical slowing down in training diffusion models
Luca Maria Del Bono, Giulio Biroli, Patrick Charbonneau +1
Computational sampling has been central to the sciences since the mid-20th century. While machine-learning-based approaches have recently enabled major advances, their behavior rem…
Discontinuous BBP transitions
Dario Bocchi, Giulio Biroli, Chiara Cammarota +1
The Baik-Ben Arous-Peche (BBP) transition sets fundamental limits for detecting low-rank structure in noisy high-dimensional data and underlies a wide range of spectral methods in…
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…