49 citations · 51 across the 7 of their papers we have counts for
7 papers
Fundamental operating regimes, hyper-parameter fine-tuning and glassiness: towards an interpretable replica-theory for trained restricted Boltzmann machines
Alberto Fachechi, Elena Agliari, Miriam Aquaro +2
We consider restricted Boltzmann machines with a binary visible layer and a Gaussian hidden layer trained by an unlabelled dataset composed of noisy realizations of a single ground…
Learning in Associative Networks through Pavlovian Dynamics
Daniele Lotito, Miriam Aquaro, Chiara Marullo
Hebbian learning theory is rooted in Pavlov's Classical Conditioning. While mathematical models of the former have been proposed and studied in the past decades, especially in spin…
Regularization, early-stopping and dreaming: a Hopfield-like setup to address generalization and overfitting
Elena Agliari, Francesco Alemanno, Miriam Aquaro +1
In this work we approach attractor neural networks from a machine learning perspective: we look for optimal network parameters by applying a gradient descent over a regularized los…
Ultrametric identities in glassy models of Natural Evolution
Elena Agliari, Francesco Alemanno, Miriam Aquaro +1
Spin-glasses constitute a well-grounded framework for evolutionary models. Of particular interest for (some of) these models is the lack of self-averaging of their order parameters…
Pavlov Learning Machines
Elena Agliari, Miriam Aquaro, Adriano Barra +2
As well known, Hebb's learning traces its origin in Pavlov's Classical Conditioning, however, while the former has been extensively modelled in the past decades (e.g., by Hopfield…
Recurrent neural networks that generalize from examples and optimize by dreaming
Miriam Aquaro, Francesco Alemanno, Ido Kanter +3
The gap between the huge volumes of data needed to train artificial neural networks and the relatively small amount of data needed by their biological counterparts is a central puz…
Supervised Hebbian Learning
Francesco Alemanno, Miriam Aquaro, Ido Kanter +2
In neural network's Literature, Hebbian learning traditionally refers to the procedure by which the Hopfield model and its generalizations store archetypes (i.e., definite patterns…