Compressing invariant manifolds in neural nets. - Jonas Paccolat; Leonardo Petrini; Mario Geiger; Kevin Tyloo; Matthieu Wyart (CoRR, 2020)

Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training. - Mario Geiger; Leonardo Petrini; Matthieu Wyart (CoRR, 2020)

Relative stability toward diffeomorphisms indicates performance in deep nets. - Leonardo Petrini; Alessandro Favero; Mario Geiger; Matthieu Wyart (NeurIPS, 2021)

Relative stability toward diffeomorphisms in deep nets indicates performance. - Leonardo Petrini; Alessandro Favero; Mario Geiger; Matthieu Wyart (CoRR, 2021)

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

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

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations. - Leonardo Petrini (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)