45 citations · 321 across the 24 of their papers we have counts for
25 papers
Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers
Federico Bassetti, Marco Gherardi, Alessandro Ingrosso +2
Deep linear networks have been extensively studied, as they provide simplified models of deep learning. However, little is known in the case of finite-width architectures with mult…
Resolution of similar patterns in a solvable model of unsupervised deep learning with structured data
Andrea Baroffio, Pietro Rotondo, Marco Gherardi
Empirical data, on which deep learning relies, has substantial internal structure, yet prevailing theories often disregard this aspect. Recent research has led to the definition of…
Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalisation
Simone Ciceri, Lorenzo Cassani, Matteo Osella +3
To achieve near-zero training error in a classification problem, the layers of a feed-forward network have to disentangle the manifolds of data points with different labels, to fac…
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…
Self-induced glassy phase in multimodal cavity quantum electrodynamics
Vittorio Erba, Mauro Pastore, Pietro Rotondo
We provide strong evidence that the effective spin-spin interaction in a multimodal confocal optical cavity gives rise to a self-induced glassy phase, which emerges exclusively fro…