Federica Gerace | DeepAI

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∙ 04/14/2023

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

Transformers are the type of neural networks that has revolutionised nat...

0 Riccardo Rende, et al. ∙

∙ 03/02/2023

Optimal transfer protocol by incremental layer defrosting

Transfer learning is a powerful tool enabling model training with limite...

0 Federica Gerace, et al. ∙

∙ 05/31/2022

Inducing bias is simpler than you think

Machine learning may be oblivious to human bias but it is not immune to ...

0 Stefano Sarao Mannelli, et al. ∙

∙ 05/26/2022

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

While classical in many theoretical settings, the assumption of Gaussian...

8 Federica Gerace, et al. ∙

∙ 06/09/2021

Probing transfer learning with a model of synthetic correlated datasets

Transfer learning can significantly improve the sample efficiency of neu...

0 Federica Gerace, et al. ∙

∙ 02/21/2020

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

We study generalised linear regression and classification for a syntheti...

28 Federica Gerace, et al. ∙

∙ 02/01/2019

Signal propagation in continuous approximations of binary neural networks

The training of stochastic neural network models with binary (±1) weight...

0 George Stamatescu, et al. ∙

∙ 10/26/2017

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

Stochasticity and limited precision of synaptic weights in neural networ...

0 Carlo Baldassi, et al. ∙

∙ 02/12/2016

Learning may need only a few bits of synaptic precision

Learning in neural networks poses peculiar challenges when using discret...

0 Carlo Baldassi, et al. ∙

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