| Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data UM Tomasini, A Sclocchi, M Wyart International Conference on Machine Learning, 21548-21583, 2022 | 10* | 2022 |
| Predictors and predictands of linear response in spatially extended systems UM Tomasini, V Lucarini The European Physical Journal Special Topics 230 (14), 2813-2832, 2021 | 5 | 2021 |
| How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model L Petrini, F Cagnetta, UM Tomasini, A Favero, M Wyart arXiv preprint arXiv:2307.02129, 2023 | 4* | 2023 |
| How deep convolutional neural networks lose spatial information with training UM Tomasini, L Petrini, F Cagnetta, M Wyart Machine Learning: Science and Technology 4 (4), 045026, 2023 | 3 | 2023 |
| How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model U Tomasini, M Wyart arXiv preprint arXiv:2404.10727, 2024 | | 2024 |
| Using Observables as Predictors through Response Theory: From Linear Systems to Nonlinear Climate Models UM Tomasini | | 2020 |
| Estensioni centrali e Anomalie in Meccanica Quantistica UM Tomasini | | |
