35 citations · 49 across the 5 of their papers we have counts for
5 papers
Spontaneous and Collective Learning in a Minimal Agent-Environment Model
Sebastiano Ariosto, Jerome Garnier-Brun, Luca Saglietti +1
How can information be extracted from data without explicit guidance or rewards? We investigate this question in a minimal setting where a classifying agent is exposed to a stream…
Statistical Physics of Deep Neural Networks: Generalization Capability, Beyond the Infinite Width, and Feature Learning
Sebastiano Ariosto
Deep Neural Networks (DNNs) excel at many tasks, often rivaling or surpassing human performance. Yet their internal processes remain elusive, frequently described as "black boxes."…
A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit
R. Pacelli, S. Ariosto, M. Pastore +3
Despite the practical success of deep neural networks, a comprehensive theoretical framework that can predict practically relevant scores, such as the test accuracy, from knowledge…
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…
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…