Locality defeats the curse of dimensionality in convolutional teacher-student scenarios. - Alessandro Favero; Francesco Cagnetta; Matthieu Wyart (NeurIPS, 2021)

PhyDSLK: a model-driven framework for generating exergames. - Maria Teresa Baldassarre; Danilo Caivano; Simone Romano 001; Francesco Cagnetta; Víctor Fernández-Cervantes; Eleni Stroulia (Multim. Tools Appl., 2021)

Learning sparse features can lead to overfitting in neural networks. - Leonardo Petrini; Francesco Cagnetta; Eric Vanden-Eijnden; Matthieu Wyart (NeurIPS, 2022)

How Wide Convolutional Neural Networks Learn Hierarchical Tasks. - Francesco Cagnetta; Alessandro Favero; Matthieu Wyart (CoRR, 2022)

What Can Be Learnt With Wide Convolutional Neural Networks? - Francesco Cagnetta; Alessandro Favero; Matthieu Wyart (ICML, 2023)

How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model. - Leonardo Petrini; Francesco Cagnetta; Umberto M. Tomasini; Alessandro Favero; Matthieu Wyart (CoRR, 2023)

Kernels, Data & Physics. - Francesco Cagnetta; Deborah Oliveira; Mahalakshmi Sabanayagam; Nikolaos Tsilivis 002; Julia Kempe (CoRR, 2023)

How deep convolutional neural networks lose spatial information with training. - Umberto M. Tomasini; Leonardo Petrini; Francesco Cagnetta; Matthieu Wyart (Mach. Learn. Sci. Technol., 2023)

Towards a theory of how the structure of language is acquired by deep neural networks. - Francesco Cagnetta; Matthieu Wyart (NeurIPS, 2024)

How compositional generalization and creativity improve as diffusion models are trained. - Alessandro Favero; Antonio Sclocchi; Francesco Cagnetta; Pascal Frossard; Matthieu Wyart (CoRR, 2025)

Learning curves theory for hierarchically compositional data with power-law distributed features. - Francesco Cagnetta; Hyunmo Kang; Matthieu Wyart (CoRR, 2025)

Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures. - Francesco Cagnetta; Alessandro Favero; Antonio Sclocchi; Matthieu Wyart (CoRR, 2025)