Committee 
Date Time 
Place 
Paper Title / Authors 
Abstract 
Paper # 
IBISML 
20200109 16:20 
Tokyo 
ISM 
Evaluation of effective degree of freedom of physical model by learning capacity Yohichi Mototake (ISM), Kenji Nagata (NIMS) IBISML201925 
[more] 
IBISML201925 pp.5354 
MBE, NC 
20191011 15:00 
Miyagi 

Analysis of diffuse lung disease shadows using Bolasso feature selection method Akihiro Endo (UEC), Kenji Nagata (NIMS), Shoji Kido (Osaka Univ.), Hayaru Shouno (UEC) MBE201933 NC201924 
Diffuse lung disease is an intractable disease and abnormal shadows appear on lung Xray CT images.
Since various patte... [more] 
MBE201933 NC201924 pp.2327 
IBISML 
20181105 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) IBISML201882 
In Gaussian Mixture Model (GMM), Bayesian estimation is one of the estimation methods, but analyti cal calculation is d... [more] 
IBISML201882 pp.287292 
IBISML 
20171110 13:00 
Tokyo 
Univ. of Tokyo 
[Poster Presentation]
Proposal of λscan Method in Spectral Deconvolution Yohachi Mototake (Univ of Tokyo), Yasuhiko Igarashi (NIMS), Hikaru Takenaka (Univ of Tokyo), Kenji Nagata (AIST), Masato Okada (Univ of Tokyo) IBISML201780 
Spectral deconvolution is a method to fit spectral data as the sum of unimodal basis functions and is a useful method in... [more] 
IBISML201780 pp.325332 
IBISML 
20161116 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) IBISML201661 
We propose a novel method for effective Bayesian inference of classical spin model by the multidimensional multiple hist... [more] 
IBISML201661 pp.109116 
IBISML 
20161117 14:00 
Kyoto 
Kyoto Univ. 
An Exhaustive Search with Support Vector Machine (ESSVM) 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) IBISML201696 
Nagata et al.(2015) has proposed Exhaustive Search with Support Vector Machine(ESSVM) which calculates a cross validati... [more] 
IBISML201696 pp.361368 
IBISML 
20151127 14:00 
Ibaraki 
Epochal Tsukuba 
[Poster Presentation]
Minimum required data amount in Bayesian inference from the viewpoint of specific heat Satoru Tokuda, Kenji Nagata, Masato Okada (Univ. of Tokyo) IBISML201574 
The accuracy of Bayesian inference depends on the number of samples or noise. Sample size or noise level often changes t... [more] 
IBISML201574 pp.159166 
MI 
20150908 13:00 
Tokyo 
Univ. of Electrocommunications 
[Special Talk]
An exhaustive search for feature selection by using MCMC method Kenji Nagata (Univ. Tokyo) MI201551 
[more] 
MI201551 p.17 
NC, MBE 
20150317 13:00 
Tokyo 
Tamagawa University 
Latent dynamics estimation from timeseries spectral data Shin Murata, Kenji Nagata (Univ. of Tokyo), Makoto Uemura (Hiroshima Univ.), Masato Okada (Univ. of Tokyo/RIKEN) MBE2014173 NC2014124 
Estimation of latent dynamics from timeseries data is important problem in a broad range of fields. In this research, w... [more] 
MBE2014173 NC2014124 pp.319324 
SIP 
20140828 13:30 
Osaka 
Ritsumeikan Univ. (Osaka Umeda Campus) 
[Invited Talk]
Bayesian estimation with the exchange Monte Carlo method and datadriven science Kenji Nagata (Univ. of Tokyo) SIP201476 
[more] 
SIP201476 p.25 
NC, MBE (Joint) 
20140318 10:20 
Tokyo 
Tamagawa University 
Theory of firing distribution with common noise originating from synaptic connectivity Ryo Karakida, Yasuhiko Igarashi, Kenji Nagata (Univ. of Tokyo), Masato Okada (Univ. Tokyo/RIKEN) NC2013103 
Common noise is a correlated noise input shared by multiple neurons. Biological experiments have shown that the common n... [more] 
NC2013103 pp.8590 
NC, MBE (Joint) 
20140318 13:00 
Tokyo 
Tamagawa University 
Model selection of NiGa_2S_4 triangular lattice by Bayesian estimation Hikaru Takenaka, Kenji Nagata, Takashi Mizokawa (Univ. of Tokyo), Masato Okada (Univ. of Tokyo/RIKEN) NC2013107 
The superexchange interaction, which favors parallel or antiparallel alignment of electron spins in magnets, has an imp... [more] 
NC2013107 pp.109114 
NC, MBE (Joint) 
20140318 13:20 
Tokyo 
Tamagawa University 
Bayesian estimation for spectral deconvolution and estimation for limit of measurement time Kenji Nagata, Rei Muraoka, Takehiko Sasaki, Masato Okada (Univ. of Tokyo) NC2013108 
[more] 
NC2013108 pp.115120 
NC, MBE (Joint) 
20140318 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) NC2013109 
In the models with hierarchy like artificial neural networks and mixture models, asymptotic normality, which AIC and BIC... [more] 
NC2013109 pp.121126 
NC, MBE (Joint) 
20131122 14:15 
Miyagi 
Tohoku University 
Verification of Effectiveness of the Probabilistic Algorithm for Latent Structure Extraction Using Associative Memory Model Kensuke Wakasugi (Univ. Tokyo), Tatsu Kuwatani (Tohoku Univ.), Kenji Nagata (Univ. Tokyo), Hideki Asoh (AIST), Masato Okada (Univ. Tokyo/RIKEN) NC201349 
There are many analysis methods for highdimensional data, but, in many cases, analysis is done under assumption that a ... [more] 
NC201349 pp.1722 
IBISML 
20131113 15:45 
Tokyo 
Tokyo Institute of Technology, KuramaeKaikan 
[Poster Presentation]
Efficient Exhaustive Search for Variable Selection with Exchange Monte Carlo Method Kenji Nagata (Univ. of Tokyo), Jun Kitazono (JST), Shinichi Nakajima (Nikon), Satoshi Eifuku (Fukushima Medical Univ.), Ryoi Tamura (Univ. of Toyama), Masato Okada (Univ. of Tokyo) IBISML201361 
[more] 
IBISML201361 pp.191196 
NC, MBE (Joint) 
20121212 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) NC201278 
Radial basis function (RBF) network is a regression model which regresses inputoutput data by radial basis functions su... [more] 
NC201278 pp.2530 
NC, MBE (Joint) 
20121212 11:05 
Aichi 
Toyohashi University of Technology 
Instabilities of spurious state with synaptic depression Shin Murata, Yosuke Otsubo, Kenji Nagata (Univ. of Tokyo), Masato Okada (Univ. of Tokyo/BSI RIKEN) NC201279 
The associative memory model is one of typical neural network model and has equilibrium state called spurious state in w... [more] 
NC201279 pp.3136 
NC, MBE (Joint) 
20121212 15:40 
Aichi 
Toyohashi University of Technology 
Distribution estimation of hyperparameters in Markov random field model Yoshinori Ohno, Kenji Nagata (Univ. Tokyo), Hayaru Shouno (UEC), Masato Okada (Univ. Tokyo/RIKEN) NC201286 
Recent advances in measurement techniques allow us to obtain a large quantity of imaging data in various natural science... [more] 
NC201286 pp.5560 
NC, MBE (Joint) 
20121212 16:05 
Aichi 
Toyohashi University of Technology 
Sparse Estimation of SpikeTriggered Average Shimpei Yotsukura (Univ. of Tokyo), Toshiaki Omori (Kobe Univ.), Kenji Nagata, Masato Okada (Univ. of Tokyo) NC201287 
Spike triggered average (STA) and phase response curve characterize response properties of single neurons. A recent theo... [more] 
NC201287 pp.6166 