Ph.D. in Fisica , 36th cycle (2020-2023)
Ph.D. obtained in 2024
Dissertation:
A data-agnostic statistical mechanics approach for studying deep neural networks beyond the infinite-width limit (Abstract)
Tutors:
Riccardo ZecchinaPublications
Works published during the Ph.D. View all publications in Porto@Iris
- Pacelli, Rosalba (2024)
A data-agnostic statistical mechanics approach for studying deep neural networks beyond the infinite-width limit. relatore: ZECCHINA, RICCARDO; , 36. XXXVI Ciclo, P.: 154
Doctoral Thesis - Baldassi, C.; Lauditi, C.; Malatesta, E. M.; Pacelli, R.; Perugini, G.; Zecchina, R. (2022)
Learning through atypical phase transitions in overparameterized neural networks. In: PHYSICAL REVIEW. E, vol. 106. ISSN 2470-0053
Contributo su Rivista - Ariosto, S.; Pacelli, R.; Ginelli, F.; Gherardi, M.; Rotondo, P. (2022)
Universal mean-field upper bound for the generalization gap of deep neural networks. In: PHYSICAL REVIEW. E, vol. 105. ISSN 2470-0053
Contributo su Rivista