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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 6 of 6  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
SIP, IT, RCS 2021-01-22
15:15
Online Online An Image Generative Model with Various Auto-regressive Coefficients Depending on Neighboring Pixels and the Bayes Code for It
Masahiro Takano, Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2020-108 SIP2020-86 RCS2020-199
In this papar, we propose an expanded model of an autoregressive stochastic generative model for images. This model cont... [more] IT2020-108 SIP2020-86 RCS2020-199
pp.253-258
IT 2020-12-02
10:00
Online Online Policy Optimization Based on Bayesian Decision Theory in Learning Period on Markov Decision Process
Naoki Ichijo, Yuta Nakahara, Yuto Motomura, Toshiyasu Matsushima (Waseda Univ.) IT2020-31
In Markov decision process(MDP) problems with an unknown transition probability, a learning agent has to learn the unkno... [more] IT2020-31
pp.38-43
IT, EMM 2020-05-28
15:25
Online Online An Autoregressive Image Generative Model and the Bayes Code for It
Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2020-4 EMM2020-4
In this paper, we propose an autoregressive stochastic generative model for images.
This model should be one of the mos... [more]
IT2020-4 EMM2020-4
pp.19-24
MI, IE, SIP, BioX, ITE-IST, ITE-ME [detail] 2020-05-29
14:10
Online Online Construction of Hidden Markov Models for Brain Tumor Segmentation
Takuya Honda, Yuta Nakahara, Matushima Toshiyasu (Waseda Univ.) SIP2020-13 BioX2020-13 IE2020-13 MI2020-13
Brain tumor segmentation is one of the systems that a computer, which has attracted attention in recent years, assists d... [more] SIP2020-13 BioX2020-13 IE2020-13 MI2020-13
pp.61-66
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
RCS, IT, SIP 2016-01-18
11:00
Osaka Kwansei Gakuin Univ. Osaka Umeda Campus A Study on Message Passing Algorithm for Counting Short Cycles in Sparse Bipartite Graphs
Yuta Nakahara, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2015-50 SIP2015-64 RCS2015-282
In this paper, we propose an improvement of message passing algorithm for counting short cycles in sparse bipartite grap... [more] IT2015-50 SIP2015-64 RCS2015-282
pp.13-18
 Results 1 - 6 of 6  /   
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