Learning a spin glass: determining Hamiltonians from metastable states
Disordered Systems and Neural Networks
2010-06-10 v1
Abstract
We study the problem of determining the Hamiltonian of a fully connected Ising Spin Glass of units from a set of measurements, whose sizes needs to be bits. The student-teacher scenario, used to study learning in feed-forward neural networks, is here extended to spin systems with arbitrary couplings. The set of measurements consists of data about the local minima of the rugged energy landscape. We compare simulations and analytical approximations for the resulting learning curves obtained by using different algorithms.
Cite
@article{arxiv.cond-mat/9712177,
title = {Learning a spin glass: determining Hamiltonians from metastable states},
author = {Silvia Kuva and Osame Kinouchi and Nestor Caticha},
journal= {arXiv preprint arXiv:cond-mat/9712177},
year = {2010}
}
Comments
5 pages, 1 figure, to appear in Physica A