3 citations · 3 across the 3 of their papers we have counts for
3 papers
cond-mat.dis-nn2025
Microscopic and collective signatures of feature learning in neural networks
Andrea Corti, Rosalba Pacelli, Pietro Rotondo +1
Feature extraction - the ability to identify relevant properties of data - is a key factor underlying the success of deep learning. Yet, it has proved difficult to elucidate its na…
cond-mat.dis-nn2024
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.…
cs.LG2023★ 3 cited
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…