Measuring logic complexity can guide pattern discovery in empirical systems. - Marco Gherardi; Pietro Rotondo (Complex., 2016)

Counting the learnable functions of structured data. - Pietro Rotondo; Marco Cosentino Lagomarsino; Marco Gherardi (CoRR, 2019)

Intrinsic dimension estimation for locally undersampled data. - Vittorio Erba; Marco Gherardi; Pietro Rotondo (CoRR, 2019)

Beyond the storage capacity: data driven satisfiability transition. - Pietro Rotondo; Mauro Pastore; Marco Gherardi (CoRR, 2020)

Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalisation. - Simone Ciceri; Lorenzo Cassani; Pierre Pizzochero; Matteo Osella; Pietro Rotondo; Marco Gherardi (CoRR, 2023)

Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks. - Riccardo Aiudi; R. Pacelli; Alessandro Vezzani; Raffaella Burioni; Pietro Rotondo (CoRR, 2023)

A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit. - R. Pacelli; Sebastiano Ariosto; Mauro Pastore; Francesco Ginelli; Marco Gherardi; Pietro Rotondo (Nat. Mac. Intell., 2023)

Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers. - Federico Bassetti; Marco Gherardi; Alessandro Ingrosso; Mauro Pastore; Pietro Rotondo (CoRR, 2024)

Proportional infinite-width infinite-depth limit for deep linear neural networks. - Federico Bassetti; Lucia Ladelli; Pietro Rotondo (CoRR, 2024)

Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalization. - Simone Ciceri; Lorenzo Cassani; Matteo Osella; Pietro Rotondo; Filippo Valle; Marco Gherardi (Nat. Mac. Intell., 2024)