35 citations · 67 across the 7 of their papers we have counts for
3 papers · 1 filter
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…
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.…