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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MSS, CAS, SIP, VLD | 2023-07-06 14:40 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Convergence Acceleration of Particle-based Variational Inference by Deep Unfolding Yuya Kawamura, Satoshi Takabe (Tokyo Tech)  CAS2023-8 VLD2023-8 SIP2023-24 MSS2023-8 |
Stein Variational Gradient Descent(SVGD) is a prominent particle-based variational inference method used for estimating ... [more] | CAS2023-8 VLD2023-8 SIP2023-24 MSS2023-8 pp.37-42 |
| RCS | 2022-06-16 14:30 |
Okinawa | University of the Ryukyus, Senbaru Campus and online (Primary: On-site, Secondary: Online) |
Comparison of Performance and Complexity for different Search Methods in Stochastic MIMO Signal Detection Hiroki Asumi, Yukiko Kasuga, Kazushi Matsumura, Junichiro Hagiwara, Toshihiko Nishimura, Takanori Sato, Yasutaka Ogawa, Takeo Ohgane (Hokkaido Univ.) RCS2022-49 |
In large-scale MIMO signal detection, the computational complexity increases as the number of antennas increases. We hav... [more] | RCS2022-49 pp.150-155 |
| IT | 2021-07-09 13:00 |
Online | Online | Bayesian Optimal Prediction and Its Approximation Algorithm for the Difference of Response Variables with and without Measures Considering Individual Differences by Assuming Latent Clusters Taisuke Ishiwatari (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IT2021-23 |
In observational studies, there are problems such as "the measure can be given only once to the target" and "the charact... [more] | IT2021-23 pp.45-50 |
| IT | 2021-07-09 13:25 |
Online | Online | A Note on the Reduction of Computational Complexity for Linear Regression Model Including Cluster Explanatory Variables and Regression Explanatory Variables
-- Bayes Optimal Prediction and Sub-Optimal Algorithm -- Sho Kayama (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IT2021-24 |
By considering the probability model with the structure that the data is divided into clusters and each cluster has an i... [more] | IT2021-24 pp.51-56 |
| RCS | 2021-06-23 09:40 |
Online | Online | A Comparison of Variational Bayesian and Expectation Propagation Methods for Massive MIMO Signal Detection Hiroki Asumi, Junichiro Hagiwara, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa, Takanori Sato (Hokkaido Univ.) RCS2021-30 |
Signal detection in massive MIMO has difficulty in reducing computational complexity as the number of antennas increases... [more] | RCS2021-30 pp.7-12 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Inference for Logitistic Regression Mixture Model with Local Variational Approximation and Study for Variational Free Energy Fumito Nakamura, Ryosuke Konishi (Generic Solution), Yasushi Kiyoki (Keio) IBISML2018-48 |
A logistic regression mixture model (LRMM) is a mixed model of the Logistic regression model, and it is widely used in t... [more] | IBISML2018-48 pp.29-36 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Variational Approximation Accuracy in Non-negative Matrix Factorization Naoki Hayashi (MSI) IBISML2018-51 |
The asymptotic behavior of the variational free energy of the non-negative matrix factorization (NMF) has been elucidate... [more] | IBISML2018-51 pp.53-60 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Comparison of Bayes estimation and variational Bayes estimation in mixed normal distribution model Tomofumi Nakayama, Naoki Fujii (UT), Kenji Nagata (AIST/JST PRESTO), Masato Okada (UT) IBISML2018-82 |
In Gaussian Mixture Model (GMM), Bayesian estimation is one of the estimation methods, but analyti- cal calculation is d... [more] | IBISML2018-82 pp.287-292 |
| AI | 2018-08-27 15:50 |
Osaka | Bayesian Inference for Field of Physical Quantity from Data obtained at several Locations Masato Ota, Takeshi Okadome (KG Univ.) AI2018-23 |
This paper proposes a novel method for estimating the physical quantity at every location (physical quan- tity field) fr... [more] | AI2018-23 pp.55-60 |
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| IBISML | 2018-03-05 17:25 |
Fukuoka | Nishijin Plaza, Kyushu University | Bayesian Independent Component Analysis under Hierarchical Model on Latent Variables Kai Asaba, Shota Saito, Shunsuke Horii, Toshiyasu Matsushima (Waseda Univ.) IBISML2017-97 |
Independent component analysis (ICA) deals with the problem of estimating unknown latent variables which generate the ob... [more] | IBISML2017-97 pp.49-53 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2017-09-15 10:30 |
Tokyo | Experimental Analysis of Variational Bayesian Method in Model Selection of Gaussian Mixture Model by Singular Bayesian Information Criterion Naoki Hayashi (Tokyo Tech), Fumito Nakamura (Bosch) PRMU2017-41 IBISML2017-13 |
A Gaussian mixture model (GMM) is a statistical model used in various fields such a pattern recognition, thus, it is imp... [more] | PRMU2017-41 IBISML2017-13 pp.19-26 |
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| SANE | 2016-11-24 17:10 |
Overseas | National Taipei University of Technology (NTUT) | An Experimental study on the Effectiveness of Hierarchical-Bayesian Compressed Sensing for High-Accuracy and Robust TOA Estimation Akira Moro, Guanghao Sun, Tetsuo Kirimoto (UEC) SANE2016-76 |
Microwave radar system is a promising tool for observation of terrain surface and surveillance of suspicious ship under ... [more] | SANE2016-76 pp.127-132 |
| NC, MBE (Joint) |
2014-03-18 14:20 |
Tokyo | Tamagawa University | 3D Superresolution of Microscopic Images based on Variational Bayesian Inference via Chebyshev polynomials approximation Yasuhiro Imoto, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2013-111 |
Optical microscopes are used to elucidate changes in cellular functions mediated by morphological changes of cells in vi... [more] | NC2013-111 pp.133-138 |
| IBISML | 2014-03-06 14:55 |
Nara | Nara Women's University | Consideration of Correlation between Users' Evaluating Values and Their Dropouts in Missing Value Prediction Kenta Nishimura, Toshiyuki Tanaka (Kyoto Univ.) IBISML2013-71 |
In user-item relational data, there are sometimes correlations between values and their dropouts. Existing methods under... [more] | IBISML2013-71 pp.31-38 |
| 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 |
| IBISML | 2013-11-13 15:45 |
Tokyo | Tokyo Institute of Technology, Kuramae-Kaikan | [Poster Presentation]
Probabilistic Partial Canonical Correlation Analysis Yusuke Mukuta, Tatsuya Harada (Univ. of Tokyo) IBISML2013-58 |
Partial Canonical Correlation Analysis (Partial CCA) is a statistical method to estimate a pair of linear projections on... [more] | IBISML2013-58 pp.169-176 |
| 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 |
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| IBISML | 2012-11-07 15:30 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | Bayesian image super-resolution of large image with a compound MRF and estimating registration parameters Toshiki Kinoshita, Seiji Miyoshi (Kansai Univ.) IBISML2012-35 |
Super-resolution is a technique to estimate a higher resolution image from low-resolution images. In this manuscript, we... [more] | IBISML2012-35 pp.9-16 |
| 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 |
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