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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2014-11-18 15:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Asymptotic Analysis of Variational Bayesian Latent Dirichlet Allocation Shinichi Nakajima (TU Berlin), Issei Sato, Masashi Sugiyama (Univ. of Tokyo), Kazuho Watanabe (Toyohashi Univ. of Tech.), Hiroko Kobayashi (Nikon)  IBISML2014-64 |
Latent Dirichlet allocation (LDA) is a popular generative model of various objects such as texts and images, where an ... [more] |
IBISML2014-64 pp.219-226 |
| SP, IPSJ-MUS | 2014-05-24 11:30 |
Tokyo | Underdetermined Blind Separation of Moving Sources Based on Probabilistic Modeling Takuya Higuchi, Norihiro Takamune, Tomohiko Nakamura (Univ. of Tokyo), Hirokazu Kameoka (Univ. of Tokyo/NTT) SP2014-20 |
This paper deals with the problem of the underdetermined blind separation and tracking of moving sources. In practical s... [more] | SP2014-20 pp.211-216 |
|
| AP (2nd) |
2014-05-15 - 2014-05-16 |
Ishikawa | The Kanazawa Theatre | [Poster Presentation]
Study on Wave Distribution Function Method with Markov Random Field Model for VLF Electromagnetic Waves Mamoru Ota, Yoshiya Kasahara, Yoshitaka Goto (Kanazawa Univ.) |
The wave distribution function (WDF) method was proposed for VLF waves in the earth's plasmasphere/magnetosphere. This m... [more] | |
| IBISML | 2013-11-12 15:45 |
Tokyo | Tokyo Institute of Technology, Kuramae-Kaikan | [Poster Presentation]
Global Solvers for Variational Bayesian Low-rank Subspace Clustering Shinichi Nakajima (Nikon), Akiko Takeda (Univ. of Tokyo), S. Derin Babacan (Google), Masashi Sugiyama (Tokyo Inst. of Tech.), Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2013-37 |
Variational Bayesian (VB) learning, known to be a promising approximation method to Bayesian learning, is generally per... [more] |
IBISML2013-37 pp.7-14 |
| PRMU | 2013-02-22 10:00 |
Osaka | Image recognition based on hidden Markov eigen-image models with the variational Bayesian method Kei Sawada, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) PRMU2012-165 |
This paper proposes an image recognition technique based on Hidden Markov Eigen-image Models (HMEMs) using the variation... [more] | PRMU2012-165 pp.155-160 |
|
| IBISML | 2012-11-07 15:30 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | A Calculating Method for Estimation Accuracy of Latent Variables Based on the Free Energy Functions Keisuke Yamazaki (Tokyo Tech), Kazuho Watanabe (NAIST), Daisuke Kaji (Konicaminolta MG) IBISML2012-44 |
(To be available after the conference date) [more] | IBISML2012-44 pp.75-81 |
| IBISML | 2012-11-08 15:00 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | On Dimensionality Recovery Guarantee of Variational Bayesian PCA Shinichi Nakajima (Nikon), Ryota Tomioka (Univ. of Tokyo), Masashi Sugiyama (Tokyo Inst. of Tech.), Derin Babacan (Illinois Univ.) IBISML2012-66 |
The variational Bayesian (VB) approach is one of the best tractable approximations to the Bayesian estimation, and it ... [more] |
IBISML2012-66 pp.229-236 |
| IBISML | 2012-11-08 15:00 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | Considering the multiplicity in mixture model brings better results Tetsuo Furukawa (Kyutech) IBISML2012-89 |
The purpose of this work is to re-examine the probabilistic model of the mixture model, and to derive the algorithm by... [more] |
IBISML2012-89 pp.395-402 |
| SANE | 2012-07-27 13:25 |
Tokyo | Navigation, trafic control and general | Estimation of distribution model of lateral navigation performance of aircraft by means of variational Bayesian method Masato Fujita (ENRI) SANE2012-42 |
Lateral deviations of aircraft from the center line of the assigned route are modeled as probabilistic distribution mode... [more] | SANE2012-42 pp.7-12 |
| MBE, NC (Joint) |
2012-03-14 14:10 |
Tokyo | Tamagawa University | Emergence of even-symmetric response property of complex cell by hierarchical Bayesian model Hiroki Yokoyama, Osamu Watanabe (Muroran Inst. Tech.) NC2011-130 |
Neurons in the primary visual cortex (V1) can be classified into two types: simple- and complex-cells. Many statistical ... [more] | NC2011-130 pp.51-56 |
| MBE, NC (Joint) |
2012-03-15 14:35 |
Tokyo | Tamagawa University | Constructing sparse generative topographic mapping using variational Bayes Nobuhiko Yamaguchi (Saga Univ.) NC2011-166 |
Generative Topographic Mapping (GTM) is a nonlinear latent variable model introduced by Bishop {\it et al.} as a data vi... [more] | NC2011-166 pp.263-268 |
| PRMU | 2011-11-25 13:45 |
Nagasaki | Face recognition based on separable lattice 2-D HMMs with variational Bayesian method Kei Sawada, Akira Tamamori, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) PRMU2011-120 |
This paper proposes an image recognition technique based on separable lattice 2-D hidden Markov models (SL2D-HMMs) with ... [more] | PRMU2011-120 pp.125-130 |
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| IBISML | 2011-11-09 15:45 |
Nara | Nara Womens Univ. | Local Minima Structure of Variational Bayes Learning Fumito Nakamura, Sumio Watanabe (Tokyo Tech) IBISML2011-43 |
Variational Bayes learning has good generalization performance as Bayes learning by relatively small computational cost... [more] |
IBISML2011-43 pp.5-10 |
| IBISML | 2011-11-09 15:45 |
Nara | Nara Womens Univ. | Generalization of Matrix Factorization for Robust PCA Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech.), Derin Babacan (Illinois Univ.) IBISML2011-61 |
Principal component analysis (PCA) can be regarded as approximating a data matrix with a low-rank one by imposing spar... [more] |
IBISML2011-61 pp.127-134 |
| IBISML | 2011-11-10 15:45 |
Nara | Nara Womens Univ. | Image Segmentation and Restoration using Switching State-Space Model and Variational Bayesian Method Ryota Hasegawa (Kansai Univ.), Ken Takiyama, Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) IBISML2011-67 |
We derive a deterministic algorithm that restores and segments image using switching state-space model and variational B... [more] | IBISML2011-67 pp.169-174 |
| IBISML | 2011-11-10 15:45 |
Nara | Nara Womens Univ. | Image segmentation and restoration by variational Bayesian method and MCMC Kenta Kayano (Kansai Univ.), Kenji Nagata, Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) IBISML2011-68 |
In this paper, we derive a deterministic algorithm that restores and segments an image by using variational Bayesian met... [more] | IBISML2011-68 pp.175-180 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2011-09-05 10:00 |
Hokkaido | Global Solution of Variational Bayesian Matrix Factorization Under Matrix-wise Independence Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech.), Derin Babacan (Illinois Univ.) PRMU2011-58 IBISML2011-17 |
Variational Bayesian matrix factorization (VBMF) efficiently approximates the posterior distribution of factorized mat... [more] |
PRMU2011-58 IBISML2011-17 pp.1-8 |
|
| NC | 2011-07-26 11:00 |
Hyogo | Graduate School of Engineering, Kobe University | Image Segmentation and Restoration using Region-Based Hidden Variables and Belief Propagation Ryota Hasegawa (Kansai Univ.), Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) NC2011-35 |
We derive a deterministic algorithm that restores and segments an image using belief propagation and a variational Bayes... [more] | NC2011-35 pp.81-86 |
| IBISML | 2011-03-29 10:30 |
Osaka | Nakanoshima Center, Osaka Univ. | On Automatic Dimensionality Selection in Probabilistic PCA Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech./JST), Derin Babacan (Illinois of.Univ.) IBISML2010-123 |
In probabilistic PCA, the fully Bayesian estimation is computationally intractable. To cope with this problem, two ty... [more] |
IBISML2010-123 pp.131-138 |
| NC, MBE (Joint) |
2011-03-08 10:40 |
Tokyo | Tamagawa University | Effect of Information Source on Cross Validation in Variational Bayes Learning Shinji Oyama, Sumio Watanabe (Tokyo Tech.) NC2010-167 |
The variational Bayes learning provides high generalization performance as the Bayes learning using a small computationa... [more] | NC2010-167 pp.235-240 |
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