The Hopfield model and the Boltzmann machine are among the most popular examples of
neural networks. The latter, widely used for classification and feature detection, is able to …

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 loss …

The standard Hopfield model for associative neural networks accounts for biological
Hebbian learning and acts as the harmonic oscillator for pattern recognition, however its …

Hopfield networks are artificial neural networks which store memory patterns on the states of
their neurons by choosing recurrent connection weights and update rules such that the …

The Hopfield model is a paradigmatic model of neural networks that has been analyzed for
many decades in the statistical physics, neuroscience, and machine learning communities …

Abstract Restricted Boltzmann machines (RBMs) with a binary visible layer of size N and a
Gaussian hidden layer of size P have been proved to be equivalent to a Hopfield neural …

We consider a three-layer Sejnowski machine and show that features learnt via contrastive
divergence have a dual representation as patterns in a dense associative memory of order …

We propose a modification of the cost function of the Hopfield model whose salient features
shine in its Taylor expansion and result in more than pairwise interactions with alternate …

Recently, spatial photonic Ising machines (SPIM) have been demonstrated to compute the
minima of Hamiltonians for large-scale spin systems. Here we propose to implement an …

In this paper we adapt the broken replica interpolation technique (developed by Francesco
Guerra to deal with the Sherrington-Kirkpatrick model, namely a pairwise mean-field spin …