6 papers
Classifications in modular restricted Boltzmann machines
Elena Agliari, Andrea Lepre, Edoardo Roscani
We consider a modular associative neural network made of Hopfield models (HMs), coupled so that intra-module interactions are Hebbian and inter-module interactions are anti-Heb…
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…
Semi-supervised Hopfield model: Theoretical and Numerical results
Linda Albanese, Andrea Ladiana, Andrea Lepre
In the daily practice of Machine Learning, fully labeled datasets are a luxury: labels demand expensive and time-consuming human annotation, whereas raw, unlabeled data can be harv…
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…
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…
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…