6 citations · 7 across the 5 of their papers we have counts for
14 papers
Universal mean field upper bound for the generalisation gap of deep neural networks
S. Ariosto, R. Pacelli, F. Ginelli +2
Modern deep neural networks (DNNs) represent a formidable challenge for theorists: according to the commonly accepted probabilistic framework that describes their performance, thes…
Criticality and conformality in the random dimer model
Sergio Caracciolo, Riccardo Fabbricatore, Marco Gherardi +3
In critical systems, the effect of a localized perturbation affects points that are arbitrarily far from the perturbation location. In this paper, we study the effect of localized…
Statistical learning theory of structured data
Mauro Pastore, Pietro Rotondo, Vittorio Erba +1
The traditional approach of statistical physics to supervised learning routinely assumes unrealistic generative models for the data: usually inputs are independent random variables…
Beyond the storage capacity: data driven satisfiability transition
Pietro Rotondo, Mauro Pastore, Marco Gherardi
Data structure has a dramatic impact on the properties of neural networks, yet its significance in the established theoretical frameworks is poorly understood. Here we compute the…
Random geometric graphs in high dimension
Vittorio Erba, Sebastiano Ariosto, Marco Gherardi +1
Many machine learning algorithms used for dimensional reduction and manifold learning leverage on the computation of the nearest neighbours to each point of a dataset to perform th…
Intrinsic dimension estimation for locally undersampled data
Vittorio Erba, Marco Gherardi, Pietro Rotondo
High-dimensional data are ubiquitous in contemporary science and finding methods to compress them is one of the primary goals of machine learning. Given a dataset lying in a high-d…