| A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit R Pacelli, S Ariosto, M Pastore, F Ginelli, M Gherardi, P Rotondo Nature Machine Intelligence 5 (12), 1497-1507, 2023 | 55* | 2023 |
| Learning through atypical phase transitions in overparameterized neural networks C Baldassi, C Lauditi, EM Malatesta, R Pacelli, G Perugini, R Zecchina Physical Review E 106 (1), 014116, 2022 | 33 | 2022 |
| Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks R Aiudi, R Pacelli, P Baglioni, A Vezzani, R Burioni, P Rotondo Nature Communications 16 (1), 568, 2025 | 16 | 2025 |
| 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, F Di Renzo, A Vezzani, R Burioni, ... Physical Review Letters 133 (2), 027301, 2024 | 11 | 2024 |
| Statistical mechanics of transfer learning in fully-connected networks in the proportional limit A Ingrosso, R Pacelli, P Rotondo, F Gerace arXiv preprint arXiv:2407.07168, 2024 | 5 | 2024 |
| Universal mean-field upper bound for the generalization gap of deep neural networks S Ariosto, R Pacelli, F Ginelli, M Gherardi, P Rotondo Physical Review E 105 (6), 064309, 2022 | 3 | 2022 |
| Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks P Baglioni, L Giambagli, A Vezzani, R Burioni, P Rotondo, R Pacelli arXiv preprint arXiv:2412.15911, 2024 | 1 | 2024 |
| Les Houches lectures on deep learning at large and infinite width Y Bahri, B Hanin, A Brossollet, V Erba, C Keup, R Pacelli, JB Simon Journal of Statistical Mechanics: Theory and Experiment 2024 (10), 104012, 2024 | 1 | 2024 |
| Kernel Shape Renormalization In Bayesian Shallow Networks: a Gaussian Process Perspective R Pacelli, L Giambagli, P Baglioni 2024 IEEE Workshop on Complexity in Engineering (COMPENG), 1-6, 2024 | | 2024 |
| A data-agnostic statistical mechanics approach for studying deep neural networks beyond the infinite-width limit R Pacelli Politecnico di Torino, 2024 | | 2024 |
Pietro RotondoUnipr, Dipartimento di Scienze Matematiche, Fisiche e InformaticheEmail verificata su unipr.it
