Quantum Annealing for Variational Bayes Inference

Simple item page
dc.creatorSato, Issei
dc.creatorKurihara, Kenichi
dc.creatorTanaka, Shu
dc.creatorNakagawa, Hiroshi
dc.creatorMiyashita, Seiji
dc.date2009-05-21
dc.date2009-05-28
dc.date.accessioned2026-07-07T13:18:27Z
dc.date.available2026-07-07T13:18:27Z
dc.descriptionThis paper presents studies on a deterministic annealing algorithm based on quantum annealing for variational Bayes (QAVB) inference, which can be seen as an extension of the simulated annealing for variational Bayes (SAVB) inference. QAVB is as easy as SAVB to implement. Experiments revealed QAVB finds a better local optimum than SAVB in terms of the variational free energy in latent Dirichlet allocation (LDA).
dc.description9 pages, 4 figures, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009) accepted
dc.identifierhttps://arxiv.org/abs/0905.3528
dc.identifierhttp://arxiv.org/abs/0905.3528
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/231434
dc.subjectDisordered Systems and Neural Networks
dc.subjectStatistical Mechanics
dc.subjectMachine Learning
dc.subjectQuantum Physics
dc.titleQuantum Annealing for Variational Bayes Inference
dc.typetext

Files

Collections

arXiv