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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 37  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2020-01-09
16:20
Tokyo ISM Evaluation of effective degree of freedom of physical model by learning capacity
Yoh-ichi Mototake (ISM), Kenji Nagata (NIMS) IBISML2019-25
 [more] IBISML2019-25
pp.53-54
MBE, NC 2019-10-11
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) MBE2019-33 NC2019-24
Diffuse lung disease is an intractable disease and abnormal shadows appear on lung X-ray CT images.
Since various patte... [more]
MBE2019-33 NC2019-24
pp.23-27
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
IBISML 2017-11-10
13:00
Tokyo Univ. of Tokyo [Poster Presentation] Proposal of λ-scan 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) IBISML2017-80
Spectral deconvolution is a method to fit spectral data as the sum of unimodal basis functions and is a useful method in... [more] IBISML2017-80
pp.325-332
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. 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
IBISML 2015-11-27
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) IBISML2015-74
The accuracy of Bayesian inference depends on the number of samples or noise. Sample size or noise level often changes t... [more] IBISML2015-74
pp.159-166
MI 2015-09-08
13:00
Tokyo Univ. of Electro-communications [Special Talk] An exhaustive search for feature selection by using MCMC method
Kenji Nagata (Univ. Tokyo) MI2015-51
 [more] MI2015-51
p.17
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
SIP 2014-08-28
13:30
Osaka Ritsumeikan Univ. (Osaka Umeda Campus) [Invited Talk] Bayesian estimation with the exchange Monte Carlo method and data-driven science
Kenji Nagata (Univ. of Tokyo) SIP2014-76
 [more] SIP2014-76
p.25
NC, MBE
(Joint)
2014-03-18
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) NC2013-103
Common noise is a correlated noise input shared by multiple neurons. Biological experiments have shown that the common n... [more] NC2013-103
pp.85-90
NC, MBE
(Joint)
2014-03-18
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) NC2013-107
The superexchange interaction, which favors parallel or anti-parallel alignment of electron spins in magnets, has an imp... [more] NC2013-107
pp.109-114
NC, MBE
(Joint)
2014-03-18
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) NC2013-108
 [more] NC2013-108
pp.115-120
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)
2013-11-22
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) NC2013-49
There are many analysis methods for high-dimensional data, but, in many cases, analysis is done under assumption that a ... [more] NC2013-49
pp.17-22
IBISML 2013-11-13
15:45
Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [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) IBISML2013-61
 [more] IBISML2013-61
pp.191-196
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
NC, MBE
(Joint)
2012-12-12
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) NC2012-79
The associative memory model is one of typical neural network model and has equilibrium state called spurious state in w... [more] NC2012-79
pp.31-36
NC, MBE
(Joint)
2012-12-12
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) NC2012-86
Recent advances in measurement techniques allow us to obtain a large quantity of imaging data in various natural science... [more] NC2012-86
pp.55-60
NC, MBE
(Joint)
2012-12-12
16:05
Aichi Toyohashi University of Technology Sparse Estimation of Spike-Triggered Average
Shimpei Yotsukura (Univ. of Tokyo), Toshiaki Omori (Kobe Univ.), Kenji Nagata, Masato Okada (Univ. of Tokyo) NC2012-87
Spike triggered average (STA) and phase response curve characterize response properties of single neurons. A recent theo... [more] NC2012-87
pp.61-66
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