6 papers
Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime
Paolo Baglioni, Christian Keup, Vincenzo Zimbardo +4
The scaling limit where both the size of the training set and the width of a deep neural network grow at the same rate, the so-called proportional-width regime, has been in…
Unveiling the Dimensionality of Networks of Networks
Lorenzo Grimaldi, Pablo Villegas, Alessandro Vezzani +3
"Every object that biology studies is a system of systems." (François Jacob, 1974). Most networks feature intricate architectures originating from tinkering, a repetitive use of e…
Pedestrian fluxes in confined geometric networks: entropic measures and robustness of accessibility in a university campus
Adamo Cerioli, Barbara Caselli, Lea Jeanne Marinelli +2
When discussing urban life, pedestrian accessibility to all main services is crucial for fostering social interactions, promoting healthy lifestyles, and reducing pollution. This i…
Rare Events and Redundancy in Random Walkers Target Search in a Finite Domain
Elisabetta Ellettari, Giacomo Nasuti, Alberto Bassanoni +2
Finding a target in a complex environment is a fundamental challenge across natural systems, from chemical reactions to sperm cells reaching an egg. A powerful strategy to reduce s…
Rare Events and Single Big Jump Effects in Ornstein-Uhlenbeck Processes
Alberto Bassanoni, Alessandro Vezzani, Eli Barkai +1
Even in a simple stochastic process, the study of the full distribution of time integrated observables can be a difficult task. This is the case of a much-studied process such as t…
Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks
P. Baglioni, L. Giambagli, A. Vezzani +3
Finite-width one hidden layer networks with multiple neurons in the readout layer display non-trivial output-output correlations that vanish in the lazy-training infinite-width lim…