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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IT, EMM | 2022-05-17 13:25 |
Gifu | Gifu University (Primary: On-site, Secondary: Online) |
A Note on Time-Varying Two-Dimensional Autoregressive Models and the Bayes Codes Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.)  IT2022-2 EMM2022-2 |
This paper proposes a two-dimensional autoregressive model with time-varying parameters as a stochastic model for explai... [more] | IT2022-2 EMM2022-2 pp.7-12 |
| IBISML | 2022-03-08 11:20 |
Online | Online | Tree-Structured Generative Model with Latent Variables and Approximate Variational Bayesian Inference Naoki Ichijo, Yuta Nakahara (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IBISML2021-33 |
[more] | IBISML2021-33 pp.19-26 |
| RCS, SIP, IT | 2022-01-21 09:00 |
Online | Online | An Approximation by Meta-Tree Boosting Method to Bayesian Optimal Prediction for Decision Tree Model Wenbin Yu, Koki Kazama, Yuta Nakahara, Naoki Ichijo (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IT2021-67 SIP2021-75 RCS2021-235 |
[more] | IT2021-67 SIP2021-75 RCS2021-235 pp.219-224 |
| 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 |
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