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20212026
most citedReplica symmetry breaking in dense neural networks

21 citations · 69 across the 17 of their papers we have counts for

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5 papers · 1 filter

cond-mat.dis-nn2026

Dense auto-hetero associative memories applied to noisy communication channels

Elena Agliari, Andrea Alessandrelli, Adriano Barra +1

Networks of interacting Hebbian networks have recently been shown to perform a task beyond associative memory, namely \emph{pattern disentanglement}: when fed with a spurious mixtu…

cond-mat.dis-nn2026

Partial annealing and pattern decorrelation in associative neural networks

Linda Albanese, Andrea Alessandrelli, Adriano Barra +2

Using the Hopfield model as a benchmark case, the present work focuses on the investigation of partially annealed associative neural networks, wherein neural dynamics is coupled to…

cond-mat.dis-nn2026

Dense Associative Memory with biased patterns: a Replica Symmetric analysis

Linda Albanese, Andrea Alessandrelli, Federico Carella

We investigate dense higher-order associative memories in the high storage regime when the stored patterns are biased, namely when the entries of the patterns are not symmetrically…

cond-mat.dis-nn2026

A Federated Many-to-One Hopfield model for associative Neural Networks

Andrea Alessandrelli, Fabrizio Durante, Andrea Ladiana +1

Federated learning enables collaborative training without sharing raw data, but struggles under client heterogeneity and streaming distribution shifts, where drift and novel data c…

cond-mat.dis-nn2026

Serial vs parallel recall in the Blume-Every-Griffiths neural networks

Linda Albanese, Andrea Alessandrelli, Adriano Barra +1

Fully connected Blume-Emery-Griffiths neural networks performing pattern recognition and associative memory have been heuristically studied in the past (mainly via the replica tric…