21 citations · 52 across the 15 of their papers we have counts for
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How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
Alessandro Favero, Antonio Sclocchi, Francesco Cagnetta +2
Natural data is often organized as a hierarchical composition of features. How many samples do generative models need in order to learn the composition rules, so as to produce a co…
Probing the Latent Hierarchical Structure of Data via Diffusion Models
Antonio Sclocchi, Alessandro Favero, Noam Itzhak Levi +1
High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitati…
A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data
Antonio Sclocchi, Alessandro Favero, Matthieu Wyart
Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized…
What Can Be Learnt With Wide Convolutional Neural Networks?
Francesco Cagnetta, Alessandro Favero, Matthieu Wyart
Understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harne…
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios
Alessandro Favero, Francesco Cagnetta, Matthieu Wyart
Convolutional neural networks perform a local and translationally-invariant treatment of the data: quantifying which of these two aspects is central to their success remains a chal…