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20062023
most citedThe mean field Ising model trough interpolating techniques

64 citations · 341 across the 34 of their papers we have counts for

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24 papers · 1 filter

cond-mat.dis-nn2023★ 10 cited

Parallel Learning by Multitasking Neural Networks

Elena Agliari, Andrea Alessandrelli, Adriano Barra +1

A modern challenge of Artificial Intelligence is learning multiple patterns at once (i.e.parallel learning). While this can not be accomplished by standard Hebbian associative neur…

cond-mat.dis-nn2023

Statistical Mechanics of Learning via Reverberation in Bidirectional Associative Memories

Martino Salomone Centonze, Ido Kanter, Adriano Barra

We study bi-directional associative neural networks that, exposed to noisy examples of an extensive number of random archetypes, learn the latter (with or without the presence of a…

cond-mat.dis-nn2023

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…

cond-mat.dis-nn2023★ 1 cited

About the de Almeida-Thouless line in neural networks

Linda Albanese, Andrea Alessandrelli, Adriano Barra +1

In this work we present a rigorous and straightforward method to detect the onset of the instability of replica-symmetric theories in information processing systems, which does not…

cond-mat.dis-nn2022★ 21 cited

Dense Hebbian neural networks: a replica symmetric picture of supervised learning

Elena Agliari, Linda Albanese, Francesco Alemanno +5

We consider dense, associative neural-networks trained by a teacher (i.e., with supervision) and we investigate their computational capabilities analytically, via statistical-mecha…

cond-mat.dis-nn2022

Dense Hebbian neural networks: a replica symmetric picture of unsupervised learning

Elena Agliari, Linda Albanese, Francesco Alemanno +5

We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, a…