Quantum annealing for variational Bayes inference

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

Quantum annealing for variational Bayes inference

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

研究成果: Conference contribution

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 18 → 2009 6 21

Other

Other	25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
Country	Canada
City	Montreal, QC
Period	09/6/18 → 09/6/21

ASJC Scopus subject areas

Artificial Intelligence
Applied Mathematics

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引用スタイル

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AU - Kurihara, Kenichi

AU - Tanaka, Shu

AU - Nakagawa, Hiroshi

AU - Miyashita, Seiji

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AB - 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).

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