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

Research output: Contribution to conference › Paper › peer-review

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).

Original language	English
Pages	479-486
Number of pages	8
Publication status	Published - 2009 Dec 1
Externally published	Yes
Event	25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 - Montreal, QC, Canada Duration: 2009 Jun 18 → 2009 Jun 21

Conference

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

ASJC Scopus subject areas

Artificial Intelligence
Applied Mathematics

Cite this

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title = "Quantum annealing for variational Bayes inference",

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).",

author = "Issei Sato and Kenichi Kurihara and Shu Tanaka and Hiroshi Nakagawa and Seiji Miyashita",

year = "2009",

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note = "25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 ; Conference date: 18-06-2009 Through 21-06-2009",

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T1 - Quantum annealing for variational Bayes inference

AU - Sato, Issei

AU - Kurihara, Kenichi

AU - Tanaka, Shu

AU - Nakagawa, Hiroshi

AU - Miyashita, Seiji

PY - 2009/12/1

Y1 - 2009/12/1

N2 - 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).

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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M3 - Paper

AN - SCOPUS:80053140580

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EP - 486

T2 - 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

Y2 - 18 June 2009 through 21 June 2009

ER -

Quantum annealing for variational Bayes inference

Abstract

Conference

ASJC Scopus subject areas

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