Quantum annealing for variational Bayes inference

Issei Sato, Kenichi Kurihara, Shu Tanaka, Hiroshi Nakagawa, Seiji Miyashita

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

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).

本文言語English
ホスト出版物のタイトルProceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
ページ479-486
ページ数8
出版ステータスPublished - 2009
外部発表はい
イベント25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 - Montreal, QC, Canada
継続期間: 2009 6 182009 6 21

Other

Other25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
CountryCanada
CityMontreal, QC
Period09/6/1809/6/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

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