Federica Gerace

According to our database, Federica Gerace authored at least 11 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks.

Eszter Székely, Lorenzo Bardone, Federica Gerace, Sebastian Goldt

CoRR, 2023

Optimal inference of a generalised Potts model by single-layer transformers with factored attention.

Riccardo Rende, Federica Gerace, Alessandro Laio, Sebastian Goldt

CoRR, 2023

Optimal transfer protocol by incremental layer defrosting.

Federica Gerace, Diego Doimo, Stefano Sarao Mannelli, Luca Saglietti, Alessandro Laio

CoRR, 2023

2022

Probing transfer learning with a model of synthetic correlated datasets.

Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe, Lenka Zdeborová

Mach. Learn. Sci. Technol., 2022

Inducing bias is simpler than you think.

Stefano Sarao Mannelli, Federica Gerace, Negar Rostamzadeh, Luca Saglietti

CoRR, 2022

Gaussian Universality of Linear Classifiers with Random Labels in High-Dimension.

Federica Gerace, Florent Krzakala, Bruno Loureiro, Ludovic Stephan, Lenka Zdeborová

CoRR, 2022

2020

Generalisation error in learning with random features and the hidden manifold model.

Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mézard, Lenka Zdeborová

Proceedings of the 37th International Conference on Machine Learning, 2020

Critical initialisation in continuous approximations of binary neural networks.

George Stamatescu, Federica Gerace, Carlo Lucibello, Ian G. Fuss, Langford B. White

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Signal propagation in continuous approximations of binary neural networks.

George Stamatescu, Federica Gerace, Carlo Lucibello, Ian G. Fuss, Langford B. White

CoRR, 2019

2018

Statistical physics of neural systems.

Federica Gerace

PhD thesis, 2018

2017

On the role of synaptic stochasticity in training low-precision neural networks.

Carlo Baldassi, Federica Gerace, Hilbert J. Kappen, Carlo Lucibello, Luca Saglietti, Enzo Tartaglione, Riccardo Zecchina

CoRR, 2017