Francesco Cagnetta

About Francesco Cagnetta

Francesco Cagnetta, With an exceptional h-index of 8 and a recent h-index of 8 (since 2020), a distinguished researcher at École Polytechnique Fédérale de Lausanne, specializes in the field of nonequilibrium statistical mechanics.

His recent articles reflect a diverse array of research interests and contributions to the field:

How deep convolutional neural networks lose spatial information with training

Kernels, Data & Physics

How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

Feature learning and overfitting in neural networks

Statistical Mechanics of Infinitely-Wide Convolutional Networks

Learning sparse features can lead to overfitting in neural networks

What can be learnt with wide convolutional neural networks?

Renormalization group study of the dynamics of active membranes: Universality classes and scaling laws

Francesco Cagnetta Information

University

Position

Collaborateur scientifique (EPFL)

Citations(all)

202

Citations(since 2020)

191

Cited By

102

hIndex(all)

8

hIndex(since 2020)

8

i10Index(all)

6

i10Index(since 2020)

5

Email

University Profile Page

Google Scholar

Francesco Cagnetta Skills & Research Interests

nonequilibrium statistical mechanics

Top articles of Francesco Cagnetta

How deep convolutional neural networks lose spatial information with training

Machine Learning: Science and Technology

2023/11/9

Kernels, Data & Physics

arXiv preprint arXiv:2307.02693

2023/7/5

Francesco Cagnetta
Francesco Cagnetta

H-Index: 6

Julia Kempe
Julia Kempe

H-Index: 24

How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

arXiv e-prints

2023/7

Feature learning and overfitting in neural networks

Bulletin of the American Physical Society

2023/3/6

Francesco Cagnetta
Francesco Cagnetta

H-Index: 6

Statistical Mechanics of Infinitely-Wide Convolutional Networks

Bulletin of the American Physical Society

2023/3/6

Learning sparse features can lead to overfitting in neural networks

Journal of Statistical Mechanics: Theory and Experiment

2023/11/15

What can be learnt with wide convolutional neural networks?

International Conference on Machine Learning, 2023

2022/8/1

Renormalization group study of the dynamics of active membranes: Universality classes and scaling laws

Physical Review E

2022/1/27

Francesco Cagnetta
Francesco Cagnetta

H-Index: 6

Universal properties of active membranes

Physical Review E

2022/1/27

Francesco Cagnetta
Francesco Cagnetta

H-Index: 6

Work fluctuations in the active Ornstein–Uhlenbeck particle model

Journal of Statistical Mechanics: Theory and Experiment

2021/12/23

Antonio Suma
Antonio Suma

H-Index: 3

Francesco Cagnetta
Francesco Cagnetta

H-Index: 6

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios

Advances in Neural Information Processing Systems

2021/12/6

Work fluctuations of self-propelled particles in the phase separated state

Journal of Physics A: Mathematical and Theoretical

2020/8/14

Active interfaces, a universal approach

2020/6/25

Francesco Cagnetta
Francesco Cagnetta

H-Index: 6

Nonequilibrium Strategy for Fast Target Search on the Genome

Physical Review Letters

2020/5/12

Kinetic roughening in active interfaces

EPJ Web of Conferences

2020/3/11

Francesco Cagnetta
Francesco Cagnetta

H-Index: 6

Efficiency of one-dimensional active transport conditioned on motility

Physical Review E

2020/2/26

See List of Professors in Francesco Cagnetta University(École Polytechnique Fédérale de Lausanne)

Co-Authors

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