24 citations · 79 across the 6 of their papers we have counts for
6 papers
Optimal transfer protocol by incremental layer defrosting
Federica Gerace, Diego Doimo, Stefano Sarao Mannelli +2
Transfer learning is a powerful tool enabling model training with limited amounts of data. This technique is particularly useful in real-world problems where data availability is o…
Bias-inducing geometries: an exactly solvable data model with fairness implications
Stefano Sarao Mannelli, Federica Gerace, Negar Rostamzadeh +1
Machine learning (ML) may be oblivious to human bias but it is not immune to its perpetuation. Marginalisation and iniquitous group representation are often traceable in the very d…
Probing transfer learning with a model of synthetic correlated datasets
Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli +2
Transfer learning can significantly improve the sample efficiency of neural networks, by exploiting the relatedness between a data-scarce target task and a data-abundant source tas…
From statistical inference to a differential learning rule for stochastic neural networks
Luca Saglietti, Federica Gerace, Alessandro Ingrosso +2
Stochastic neural networks are a prototypical computational device able to build a probabilistic representation of an ensemble of external stimuli. Building on the relationship bet…
On the role of synaptic stochasticity in training low-precision neural networks
Carlo Baldassi, Federica Gerace, Hilbert J. Kappen +4
Stochasticity and limited precision of synaptic weights in neural network models are key aspects of both biological and hardware modeling of learning processes. Here we show that a…
Learning may need only a few bits of synaptic precision
Carlo Baldassi, Federica Gerace, Carlo Lucibello +2
Learning in neural networks poses peculiar challenges when using discretized rather then continuous synaptic states. The choice of discrete synapses is motivated by biological reas…