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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| HCGSYMPO (2nd) |
2023-12-11 - 2023-12-13 |
Fukuoka | Asia pacific Import Mart (Kitakyushu) (Primary: On-site, Secondary: Online) |
Reproducing the anchoring effect with Bayesian updating Airi Ono, Fumiya Komatsu, Kazuki Takahashi, Takashi Takekawa (Kogakuin Univ.) |
The anchoring effect is a cognitive bias known for unconsciously distorting judgments based on previously presented info... [more] | |
| 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 |
| IT, RCS, SIP | 2023-01-25 14:35 |
Gunma | Maebashi Terrsa (Primary: On-site, Secondary: Online) |
An Optimal Prediction on Multilevel Coefficient Linear Regression Model by Bayes Decision Theory and Its Approximation Method Kohei Horinouchi, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-67 SIP2022-118 RCS2022-246 |
It is common practice to apply Multilevel Analysis for the data sampled from various classes. In this Analysis, it is co... [more] | IT2022-67 SIP2022-118 RCS2022-246 pp.217-222 |
| 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 |
| IT | 2020-07-16 14:45 |
Online | Online | Asymptotic Evaluation of $alpha$-divergence between VB Posterior Predictive Distribution and Bayesian Predictive Distribution Kazuki Yamada, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-14 |
In this paper, we consider the problem of determining probability distribution of $X_{n+1}$ given ${X_i }_{i=1}^{n}$ fol... [more] | IT2020-14 pp.19-23 |
| MI, IE, SIP, BioX, ITE-IST, ITE-ME [detail] | 2020-05-28 12:40 |
Online | Online | [Special Talk]
Generalization of coherent point drift and its acceleration Osamu Hirose (Kanazawa Univ.) SIP2020-1 BioX2020-1 IE2020-1 MI2020-1 |
Point set registration is to find point-to-point correspondences between point sets, each of which represents the shape ... [more] | SIP2020-1 BioX2020-1 IE2020-1 MI2020-1 pp.1-3 |
| IT | 2019-07-25 14:25 |
Tokyo | NATULUCK-Iidabashi-Higashiguchi Ekimaeten | Bayes Optimal Prediction and Its Approximative Algorithm on Model Including Cluster Explanatory Variables and Regression Explanatory Variables Haruka Murayama, Shota Saito, Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2019-16 |
In this research, data are assumed to be divided in clusters based on a part of the continuous explanatory variables, an... [more] | IT2019-16 pp.5-10 |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-17 15:00 |
Okinawa | Okinawa Institute of Science and Technology | Meta-analysis fMRI data helps robust source reconstruction of MEG measurements Keita Suzuki (NAIST), Okito Yamashita (ATR) NC2019-5 |
Functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) are the major recording means of brain act... [more] | NC2019-5 pp.21-25 |
| NC, MBE (Joint) |
2019-03-04 15:45 |
Tokyo | University of Electro Communications | Variational Bayes algorithm of region base coupled MRF with hidden phase variables Naoki Wada (Tokyo Inst. of Tech.), Masaichiro Mizumaki (JASRI), Yoshiki Seno (Saga prefectural regional industry support center), Masato Okada (The Univ. of Tokyo), Akai Ichiro (Kumamoto Univ.), Toru Aonishi (Tokyo Inst. of Tech.) NC2018-59 |
There are two methods in coupled Markov Random Field(MRF) model for image segmentation: edge-based method and region-bas... [more] | NC2018-59 pp.87-92 |
| 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 |
|
| SIP, EA, SP, MI (Joint) [detail] |
2018-03-19 10:50 |
Okinawa | On the Use of Deep Gaussian Processes for GPR-based Speech Synthesis Tomoki Koriyama, Takao Kobayashi (Tokyo Inst. of Tech.) EA2017-106 SIP2017-115 SP2017-89 |
This paper proposes a speech synthesis framework based on deep Gaussian processes (DGPs). DGP is a Bayesian deep learn... [more] |
EA2017-106 SIP2017-115 SP2017-89 pp.27-32 |
|
| 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 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | The Classification Problem in Generalized Label Noise Model Tota Suko, Shunsuke Horii (Waseda Univ) IBISML2017-87 |
In classification problem, there is a case where noise is added to the label. In this study, we proposes a general nois... [more] |
IBISML2017-87 pp.377-382 |
| 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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