34 citations · 48 across the 13 of their papers we have counts for
14 papers
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…
Exponential Capacity in Multilayer Hetero-Associative Neural Networks
Elena Agliari, Adriano Barra, Andrea Ladiana +1
Exponential Hopfield networks store a number of patterns that grows exponentially with the number of neurons, and in their classical formulation they are auto-associative: they com…
Do Hopfield Networks Dream of Stored Patterns? A Statistical-Mechanical Theory of Dreaming in Multidirectional Associative Memories
Adriano Barra, Fabrizio Durante, Andrea Ladiana +1
We introduce the Dreaming -directional Associative Memory (DLAM), a multi-layer Hebbian architecture in which off-line dreaming and supervised heteroassociative coupling coexist…
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…
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…
Yet another exponential Hopfield model
Linda Albanese, Andrea Alessandrelli, Adriano Barra +1
We propose and analyze a new variation of the so-called {\em exponential Hopfield model}, a recently introduced family of associative neural networks with unprecedented storage cap…
Supervised and Unsupervised protocols for hetero-associative neural networks
Andrea Alessandrelli, Adriano Barra, Andrea Ladiana +2
This paper introduces a learning framework for Three-Directional Associative Memory (TAM) models, extending the classical Hebbian paradigm to both supervised and unsupervised proto…
Guerra interpolation for inverse freezing
Linda Albanese, Adriano Barra, Emilio N. M. Cirillo
In these short notes, we adapt and systematically apply Guerra's interpolation techniques on a class of disordered mean-field spin glasses equipped with crystal fields and multi-va…
Beyond Disorder: Unveiling Cooperativeness in Multidirectional Associative Memories
Andrea Alessandrelli, Adriano Barra, Andrea Ladiana +2
By leveraging tools from the statistical mechanics of complex systems, in these short notes we extend the architecture of a neural network for hetero-associative memory (called thr…
Networks of neural networks: more is different
Elena Agliari, Andrea Alessandrelli, Adriano Barra +2
The common thread behind the recent Nobel Prize in Physics to John Hopfield and those conferred to Giorgio Parisi in 2021 and Philip Anderson in 1977 is disorder. Quoting Philip An…
Generalized hetero-associative neural networks
Elena Agliari, Andrea Alessandrelli, Adriano Barra +2
Auto-associative neural networks (e.g., the Hopfield model implementing the standard Hebbian prescription) serve as a foundational framework for pattern recognition and associative…
Hebbian Learning from First Principles
Linda Albanese, Adriano Barra, Pierluigi Bianco +2
Recently, the original storage prescription for the Hopfield model of neural networks -- as well as for its dense generalizations -- has been turned into a genuine Hebbian learning…
Unsupervised and Supervised learning by Dense Associative Memory under replica symmetry breaking
Linda Albanese, Andrea Alessandrelli, Alessia Annibale +1
Statistical mechanics of spin glasses is one of the main strands toward a comprehension of information processing by neural networks and learning machines. Tackling this approach,…
A Hebbian approach to complex network generation
Elena Agliari, Adriano Barra
Through a redefinition of patterns in an Hopfield-like model, we introduce and develop an approach to model discrete systems made up of many, interacting components with inner degr…