2 papers
cond-mat.dis-nn2024
Stochastic Gradient Descent-like relaxation is equivalent to Metropolis dynamics in discrete optimization and inference problems
Maria Chiara Angelini, Angelo Giorgio Cavaliere, Raffaele Marino +1
Is Stochastic Gradient Descent (SGD) substantially different from Metropolis Monte Carlo dynamics? This is a fundamental question at the time of understanding the most used trainin…
cond-mat.dis-nn2024
Daydreaming Hopfield Networks and their surprising effectiveness on correlated data
Ludovica Serricchio, Dario Bocchi, Claudio Chilin +4
To improve the storage capacity of the Hopfield model, we develop a version of the dreaming algorithm that perpetually reinforces the patterns to be stored (as in the Hebb rule), a…