Collapsed Variational Inference for HDP (2009)
Yee Whye Teh, Kenichi Kurihara, Max Welling
A wide variety of Dirichlet-multinomial ‘topic ’ models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in such models,...
Identification of MCMC Samples for Clustering (2009)
Kenichi Kurihara, Tsuyoshi Murata, Taisuke Sato
Abstract. For clustering problems, many studies use just MAP assignments to show clustering results instead of using whole samples from a MCMC sampler. This is because it is not straightforward to...
Graph Mining with Variational Dirichlet Process Mixture Models (2009)
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high dimensionality of...
Quantum Annealing for Clustering (2009)
Kurihara, Kenichi, Tanaka, Shu, Miyashita, Seiji
This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA). We derive a QA algorithm for clustering and propose an annealing schedule,...
Quantum Annealing for Variational Bayes Inference (2009)
Sato, Issei, Kurihara, Kenichi, Tanaka, Shu, Nakagawa, Hiroshi, Miyashita, Seiji
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...
Yee Whye Teh, Kenichi Kurihara, Max Welling
A wide variety of Dirichlet-multinomial ‘topic ’ models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in such models,...
Yee Whye Teh, Kenichi Kurihara, Max Welling
A wide variety of Dirichlet-multinomial ‘topic ’ models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in such models,...
Collapsed Variational Inference for HDP (2008)
Teh, Yee Whye, Kurihara, Kenichi, Welling, Max
A wide variety of Dirichlet-multinomial `topic' models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in such models, variational...
Variational Bayes via Propositionalization (2008)
Sato, Taisuke, Kameya, Yoshitaka, Kurihara, Kenichi
We propose a unified approach to VB (variational Bayes) in symbolic-statistical modeling via propositionalization. By propositionalization we mean, broadly, expressing and computing probabilistic...
Study on Plasma Shape Reproduction of Spherical (2007)
Wang, Feng, Nakamura, Kazuo, Mitarai, Osamu, Kurihara, Kenichi, Kawamata, Yoichi, Sueoka, Michiharu, ...
Cauchy-Condition Surface (CCS) method is a numerical approach to reproduce plasma shape which has good precision in conventional tokamak. In order to apply it in the plasma shape repro-duction of...
Collapsed variational Dirichlet process mixture models (2007)
Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of today’s datasets,...
Collapsed variational Dirichlet process mixture models (2007)
Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of today’s datasets,...
Collapsed variational Dirichlet process mixture models (2007)
Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of today’s datasets,...
Collapsed variational Dirichlet process mixture models (2007)
Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of today’s datasets,...
Accelerated variational dirichlet process mixtures (2006)
Kenichi Kurihara, Max Welling, Nikos Vlassis
Dirichlet Process (DP) mixture models are promising candidates for clustering applications where the number of clusters is unknown a priori. Due to computational considerations these models are...
Variational Bayesian grammar induction for natural language (2006)
Kenichi Kurihara, Taisuke Sato
Abstract. This paper presents a new grammar induction algorithm for probabilistic context-free grammars (PCFGs). There is an approach to PCFG induction that is based on parameter estimation....
We introduce a new class of “maximization expectation ” (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation and...
Accelerated variational dirichlet process mixtures (2006)
Kenichi Kurihara, Max Welling, Nikos Vlassis
Dirichlet Process (DP) mixture models are promising candidates for clustering applications where the number of clusters is unknown a priori. Due to computational considerations these models are...
We present an efficient learning algorithm for probabilistic context-free grammars based on the variational Bayesian approach. Although the maximum likelihood method has traditionally been used for...