48 citations · 50 across the 5 of their papers we have counts for
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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…
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…
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…
The Role of the Time-Dependent Hessian in High-Dimensional Optimization
Tony Bonnaire, Giulio Biroli, Chiara Cammarota
Gradient descent is commonly used to find minima in rough landscapes, particularly in recent machine learning applications. However, a theoretical understanding of why good solutio…
Dynamical Regimes of Diffusion Models
Giulio Biroli, Tony Bonnaire, Valentin de Bortoli +1
Using statistical physics methods, we study generative diffusion models in the regime where the dimension of space and the number of data are large, and the score function has been…