| Large fluctuations and dynamic phase transition in a system of self-propelled particles F Cagnetta, F Corberi, G Gonnella, A Suma Physical review letters 119 (15), 158002, 2017 | 69 | 2017 |
| Active growth and pattern formation in membrane-protein systems F Cagnetta, MR Evans, D Marenduzzo Physical review letters 120 (25), 258001, 2018 | 21 | 2018 |
| Work fluctuations of self-propelled particles in the phase separated state P Chiarantoni, F Cagnetta, F Corberi, G Gonnella, A Suma Journal of Physics A: Mathematical and Theoretical 53 (36), 36LT02, 2020 | 16 | 2020 |
| Efficiency of one-dimensional active transport conditioned on motility F Cagnetta, E Mallmin Physical Review E 101 (2), 022130, 2020 | 15 | 2020 |
| Locality defeats the curse of dimensionality in convolutional teacher-student scenarios A Favero, F Cagnetta, M Wyart Advances in Neural Information Processing Systems 34, 9456-9467, 2021 | 14 | 2021 |
| Strong anomalous diffusion of the phase of a chaotic pendulum F Cagnetta, G Gonnella, A Mossa, S Ruffo Europhysics Letters 111 (1), 10002, 2015 | 10 | 2015 |
| Kinetic roughening in active interfaces F Cagnetta, MR Evans, D Marenduzzo EPJ Web of Conferences 230, 00001, 2020 | 8 | 2020 |
| What can be learnt with wide convolutional neural networks? F Cagnetta, A Favero, M Wyart arXiv preprint arXiv:2208.01003, 2022 | 7* | 2022 |
| Statistical mechanics of a single active slider on a fluctuating interface F Cagnetta, MR Evans, D Marenduzzo Physical Review E 99 (4), 042124, 2019 | 7 | 2019 |
| Learning sparse features can lead to overfitting in neural networks L Petrini, F Cagnetta, E Vanden-Eijnden, M Wyart Advances in Neural Information Processing Systems 35, 9403-9416, 2022 | 5 | 2022 |
| Universal properties of active membranes F Cagnetta, V Škultéty, MR Evans, D Marenduzzo Physical Review E 105 (1), L012604, 2022 | 4 | 2022 |
| Nonequilibrium Strategy for Fast Target Search on the Genome F Cagnetta, D Michieletto, D Marenduzzo Physical Review Letters 124 (19), 198101, 2020 | 3* | 2020 |
| Inviscid limit of the active interface equations F Cagnetta, MR Evans Journal of Statistical Mechanics: Theory and Experiment 2019 (11), 113206, 2019 | 2 | 2019 |
| 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 | 1 | 2023 |
| How deep convolutional neural networks lose spatial information with training UM Tomasini, L Petrini, F Cagnetta, M Wyart arXiv preprint arXiv:2210.01506, 2022 | 1 | 2022 |
| Renormalization group study of the dynamics of active membranes: Universality classes and scaling laws F Cagnetta, V Škultéty, MR Evans, D Marenduzzo Physical Review E 105 (1), 014610, 2022 | 1 | 2022 |
| Work fluctuations in the active Ornstein–Uhlenbeck particle model M Semeraro, A Suma, I Petrelli, F Cagnetta, G Gonnella Journal of Statistical Mechanics: Theory and Experiment 2021 (12), 123202, 2021 | 1 | 2021 |
| Kernels, Data & Physics F Cagnetta, D Oliveira, M Sabanayagam, N Tsilivis, J Kempe arXiv preprint arXiv:2307.02693, 2023 | | 2023 |
| Feature learning and overfitting in neural networks F Cagnetta Bulletin of the American Physical Society, 2023 | | 2023 |
| Statistical Mechanics of Infinitely-Wide Convolutional Networks A Favero, F Cagnetta, M Wyart Bulletin of the American Physical Society, 2023 | | 2023 |
Davide MarenduzzoUniversity of EdinburghVerified email at ph.ed.ac.uk
