Uncertainty in Artificial Intelligence
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Quantum Annealing for Variational Bayes Inference
Issei Sato, Kenichi Kurihara, Shu Tanaka, Hiroshi Nakagawa, Seiji Miyashita
Abstract:
This 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).
Keywords: null
Pages: 479-486
PS Link:
PDF Link: /papers/09/p479-sato.pdf
BibTex:
@INPROCEEDINGS{Sato09,
AUTHOR = "Issei Sato and Kenichi Kurihara and Shu Tanaka and Hiroshi Nakagawa and Seiji Miyashita",
TITLE = "Quantum Annealing for Variational Bayes Inference",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "479--486"
}


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