50 citations · 54 across the 6 of their papers we have counts for
7 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…
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…
Proportional infinite-width infinite-depth limit for deep linear neural networks
Federico Bassetti, Lucia Ladelli, Pietro Rotondo
We study the distributional properties of linear neural networks with random parameters in the context of large networks, where the number of layers diverges in proportion to the n…
Statistical mechanics of transfer learning in fully-connected networks in the proportional limit
Alessandro Ingrosso, Rosalba Pacelli, Pietro Rotondo +1
Transfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related…
Predictive power of a Bayesian effective action for fully-connected one hidden layer neural networks in the proportional limit
P. Baglioni, R. Pacelli, R. Aiudi +4
We perform accurate numerical experiments with fully-connected (FC) one-hidden layer neural networks trained with a discretized Langevin dynamics on the MNIST and CIFAR10 datasets.…
Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks
R. Aiudi, R. Pacelli, A. Vezzani +2
Feature learning, or the ability of deep neural networks to automatically learn relevant features from raw data, underlies their exceptional capability to solve complex tasks. Howe…
Open quantum generalisation of Hopfield neural networks
P. Rotondo, M. Marcuzzi, J. P. Garrahan +2
We propose a new framework to understand how quantum effects may impact on the dynamics of neural networks. We implement the dynamics of neural networks in terms of Markovian open…