Learning and inference in a nonequilibrium Ising model with hidden nodes
2013 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, Vol. 87, no 2, 022127- p.Article in journal (Refereed) Published
We study inference and reconstruction of couplings in a partially observed kinetic Ising model. With hidden spins, calculating the likelihood of a sequence of observed spin configurations requires performing a trace over the configurations of the hidden ones. This, as we show, can be represented as a path integral. Using this representation, we demonstrate that systematic approximate inference and learning rules can be derived using dynamical mean-field theory. Although naive mean-field theory leads to an unstable learning rule, taking into account Gaussian corrections allows learning the couplings involving hidden nodes. It also improves learning of the couplings between the observed nodes compared to when hidden nodes are ignored.
Place, publisher, year, edition, pages
2013. Vol. 87, no 2, 022127- p.
Mean-Field Theory, Spin-Glass, Boltzmann Machines, Neural-Networks, Dynamics, Systems
IdentifiersURN: urn:nbn:se:kth:diva-119734DOI: 10.1103/PhysRevE.87.022127ISI: 000315151000001ScopusID: 2-s2.0-84874542191OAI: oai:DiVA.org:kth-119734DiVA: diva2:612601
QC 201303222013-03-222013-03-212013-07-01Bibliographically approved