| Sparse identification of variable star dynamics M Pasquato, M Abbas, AA Trani, M Nori, JA Kwiecinski, P Trevisan, ... The Astrophysical Journal 930 (2), 161, 2022 | 6 | 2022 |
| Measuring the spectral index of turbulent gas with deep learning from projected density maps P Trevisan, M Pasquato, A Ballone, M Mapelli Monthly Notices of the Royal Astronomical Society 498 (4), 5798-5803, 2020 | 4 | 2020 |
| Sparse logistic regression for rr lyrae versus binaries classification P Trevisan, M Pasquato, G Carenini, N Mekhaël, VF Braga, G Bono, ... The Astrophysical Journal 950 (2), 103, 2023 | 1 | 2023 |
| Interpretable machine learning for finding intermediate-mass black holes M Pasquato, P Trevisan, A Askar, P Lemos, G Carenini, M Mapelli, ... The Astrophysical Journal 965 (1), 89, 2024 | | 2024 |
| Case studies of interpretable machine learning in astrophysics P Trevisan Università degli Studi di Roma" La Sapienza", 2023 | | 2023 |
| Sparse logistic regression for RR Lyrae vs binaries classification P Trevisan, M Pasquato, G Carenini, N Mekhael, VF Braga, G Bono, ... arXiv preprint arXiv:2304.12355, 2023 | | 2023 |
| Analisi di reti dinamiche in contesto single-cell RNA-seq P TREVISAN | | |
| La rotazione delle stelle di campo e in ammassi P Trevisan | | |
| Deep Convolutional Neural Networks in Astrophysics: a case study for gas turbulence. P Trevisan | | |
