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Francesco Cagnetta
Francesco Cagnetta
Collaborateur scientifique, École polytechnique fédérale de Lausanne (EPFL)
Bestätigte E-Mail-Adresse bei epfl.ch
Titel
Zitiert von
Zitiert von
Jahr
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
782017
Active growth and pattern formation in membrane-protein systems
F Cagnetta, MR Evans, D Marenduzzo
Physical review letters 120 (25), 258001, 2018
222018
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
212021
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
192020
Efficiency of one-dimensional active transport conditioned on motility
F Cagnetta, E Mallmin
Physical Review E 101 (2), 022130, 2020
162020
What can be learnt with wide convolutional neural networks?
F Cagnetta, A Favero, M Wyart
International Conference on Machine Learning, 2023, 2022
152022
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
142022
Strong anomalous diffusion of the phase of a chaotic pendulum
F Cagnetta, G Gonnella, A Mossa, S Ruffo
Europhysics Letters 111 (1), 10002, 2015
102015
Kinetic roughening in active interfaces
F Cagnetta, MR Evans, D Marenduzzo
EPJ Web of Conferences 230, 00001, 2020
92020
Statistical mechanics of a single active slider on a fluctuating interface
F Cagnetta, MR Evans, D Marenduzzo
Physical Review E 99 (4), 042124, 2019
92019
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
F Cagnetta, L Petrini, UM Tomasini, A Favero, M Wyart
arXiv preprint arXiv:2307.02129, 2023
82023
Universal properties of active membranes
F Cagnetta, V Škultéty, MR Evans, D Marenduzzo
Physical Review E 105 (1), L012604, 2022
72022
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
52023
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
52021
Nonequilibrium Strategy for Fast Target Search on the Genome
F Cagnetta, D Michieletto, D Marenduzzo
Physical Review Letters 124 (19), 198101, 2020
4*2020
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
22022
Inviscid limit of the active interface equations
F Cagnetta, MR Evans
Journal of Statistical Mechanics: Theory and Experiment 2019 (11), 113206, 2019
22019
Active interfaces, a universal approach
F Cagnetta
The University of Edinburgh, 2020
12020
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
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