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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Hyperparameter distribution estimation for binary images with the exchange Monte Carlo method Koki Obinata, Shun Katakami, Yue Yonghao, Masato Okada (UTokyo)  IBISML2018-79 |
We estimate the distribution of hyperparameters corresponding to the coupling constant and noise in- tensity from an Isi... [more] | IBISML2018-79 pp.263-270 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Inference of Classical Spin Model by Multidimensional Multiple Histogram Method Hikaru Takenaka (UTokyo), Kenji Nagata (UTokyo/AIST/JST), Takashi Mizokawa (Waseda Univ.), Masato Okada (UTokyo/RIKEN) IBISML2016-61 |
We propose a novel method for effective Bayesian inference of classical spin model by the multidimensional multiple hist... [more] | IBISML2016-61 pp.109-116 |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | Performance comparison of natural image priors by using exchange Monte Carlo method Atsuki Matsuo, Toru Otagaki, Masato Inoue (Waseda Univ.) IBISML2016-72 |
Image processing using Bayesian framework generally needs to assume a image prior. However, there are no explicit criter... [more] | IBISML2016-72 pp.185-189 |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | Exhaustive search for sparse variable selection in linear regression Yasuhiko Igarashi, Hikaru Takenaka, Nakanishi-Ohno Yoshinori (UTokyo), Makoto Uemura (Hiroshima Univ.), Shiro Ikeda (ISM), Masato Okada (UTokyo) IBISML2016-90 |
We proposed the $K$-sparse Exhaustive-Search (ES-$K$) method, in which, assuming the optimum combination of explan... [more] |
IBISML2016-90 pp.313-320 |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | An Exhaustive Search with Support Vector Machine (ES-SVM) for sparse variable selection Daiki Kawabata (UTokyo), Hiroko Ichikawa (TUS), Yasuhiko Igarashi (UTokyo), Kenji Nagata (AIST/JST/UTokyo), Satoshi Eifuku, Ryoi Tamura (Toyama Univ.), Masato Okada (UTokyo) IBISML2016-96 |
Nagata et al.(2015) has proposed Exhaustive Search with Support Vector Machine(ES-SVM) which calculates a cross validati... [more] | IBISML2016-96 pp.361-368 |
| NC, MBE | 2015-03-17 13:00 |
Tokyo | Tamagawa University | Latent dynamics estimation from time-series spectral data Shin Murata, Kenji Nagata (Univ. of Tokyo), Makoto Uemura (Hiroshima Univ.), Masato Okada (Univ. of Tokyo/RIKEN) MBE2014-173 NC2014-124 |
Estimation of latent dynamics from time-series data is important problem in a broad range of fields. In this research, w... [more] | MBE2014-173 NC2014-124 pp.319-324 |
| QIT (2nd) |
2014-05-12 - 2014-05-13 |
Aichi | Nagoya Univ. | [Poster Presentation]
statistical mechanics formulation of prime factorization Chihiro H Nakajima (Tohoku Univ.) |
We propose a new approach to solve the problem of the prime factorization, formulating the problem as a ground state sea... [more] | |
| NC, MBE (Joint) |
2014-03-18 13:40 |
Tokyo | Tamagawa University | Computational validation of the information criterion WBIC by the exchange Monte Carlo method Satoru Tokuda, Kenji Nagata (Univ. of Tokyo), Sumio Watanabe (Tokyo Inst. of Tech.), Masato Okada (Univ. of Tokyo/RIKEN) NC2013-109 |
In the models with hierarchy like artificial neural networks and mixture models, asymptotic normality, which AIC and BIC... [more] | NC2013-109 pp.121-126 |
| NC, MBE (Joint) |
2012-12-12 10:40 |
Aichi | Toyohashi University of Technology | A numerical derivation of learning coefficient in radial basis function network Satoru Tokuda, Kenji Nagata, Masato Okada (Univ. of Tokyo) NC2012-78 |
Radial basis function (RBF) network is a regression model which regresses input-output data by radial basis functions su... [more] | NC2012-78 pp.25-30 |
| IBISML | 2011-11-09 15:45 |
Nara | Nara Womens Univ. | Adaptive Markov Chain Monte Carlo for Auxiliary Variable Method and Its Application to Exchange Monte Carlo Method Takamitsu Araki, Takashi Takenouchi, Kazushi Ikeda (NAIST) IBISML2011-47 |
For sampling from a complicated distribution, Auxiliary Variable Method, which contain Exchange Monte Carlo Method and C... [more] | IBISML2011-47 pp.33-38 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2011-09-06 15:20 |
Hokkaido | Visualization of the Phase Transition Phenomena on K-satisfiability Problem through Principal Component Analysis Yasuaki Akazawa (Waseda Univ.), Masato Okada (Univ. Tokyo), Masato Inoue (Waseda Univ.) PRMU2011-78 IBISML2011-37 |
In this manuscript, we study about the random K-satisfiability (K-SAT) problem through numerical simulation. K-SAT probl... [more] | PRMU2011-78 IBISML2011-37 pp.165-172 |
|
| NC, MBE (Joint) |
2010-03-11 17:15 |
Tokyo | Tamagawa University | Analysis of Autocorrelation type Associative Memory Model with Hierarchical Patterns by Using PCA Teijiro Shiotsuka (Waseda Univ.), Kenji Nagata, Koji Hukushima (Tokyo Univ.), Masato Okada (Tokyo Univ./RIKEN), Masato Inoue (Waseda Univ.) NC2009-158 |
The statistical mechanical approach is useful and has been applied to various problems in the field of information proce... [more] | NC2009-158 pp.413-418 |
| NC | 2006-10-11 15:00 |
Nara | NAIST | On Relation between Exchange Ratio and Kullback Divergence in Exchange Monte Carlo Method Kenji Nagata, Sumio Watanabe (T.I.Tech.) |
The exchange Monte Carlo method was proposed as an improved algorithm of Markov Chain Monte Carlo method, and its effect... [more] | NC2006-52 pp.43-48 |
| NC | 2006-03-15 11:25 |
Tokyo | Tamagawa University | Exchange Monte Carlo Method for Bayesian Learning of Singular Learning Machines Kenji Nagata, Sumio Watanabe (Tokyo Inst. of Tech.) |
A lot of singular learning machines such as neural networks, normal mixtures, Bayesian networks and hidden Markov models... [more] | NC2005-118 pp.73-78 |
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