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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NS, RCS (Joint) |
2020-12-17 11:25 |
Online | Online | Improvement on Signal Detection Performance with HMC in Massive MIMO Kazushi Matsumura, Junichiro Hagiwara, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa, Takanori Sato (Hokkaido Univ.)  RCS2020-135 |
In massive MIMO, a new technology for wireless transmission, various approaches to reduce the computational complexity a... [more] | RCS2020-135 pp.7-12 |
| RCS | 2020-06-25 14:30 |
Online | Online | A Study on Signal Detection in Massive MIMO Using MCMC Kazushi Matsumura, Junichiro Hagiwara, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa, Takanori Sato (Hokkaido Univ.) RCS2020-38 |
MIMO is a new technology for wireless transmission; as the number of antennas increases, the computational complexity of... [more] | RCS2020-38 pp.91-95 |
| NC, MBE | 2019-12-06 14:40 |
Aichi | Toyohashi Tech | Implementation of an FPGA-based energy-efficient MCMC method for 2D Lenz-Ising model Patrick Tchicali, Hayaru Shouno (UEC) MBE2019-54 NC2019-45 |
MCMC methods are arguably one of the most useful sampling methods. MCMC while being very useful and practical remains a ... [more] | MBE2019-54 NC2019-45 pp.55-60 |
| NC, MBE (Joint) |
2019-03-04 15:45 |
Tokyo | University of Electro Communications | Variational Bayes algorithm of region base coupled MRF with hidden phase variables Naoki Wada (Tokyo Inst. of Tech.), Masaichiro Mizumaki (JASRI), Yoshiki Seno (Saga prefectural regional industry support center), Masato Okada (The Univ. of Tokyo), Akai Ichiro (Kumamoto Univ.), Toru Aonishi (Tokyo Inst. of Tech.) NC2018-59 |
There are two methods in coupled Markov Random Field(MRF) model for image segmentation: edge-based method and region-bas... [more] | NC2018-59 pp.87-92 |
| 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 | 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 |
| NLP | 2016-03-25 10:25 |
Kyoto | Kyoto Sangyo Univ. | Combinatorial Optimization of Swiss System Tournaments
-- Approximation Algorithms for Set Partitioning Problem -- Sho Osako, Masato Inoue (Waseda Univ.) NLP2015-151 |
In a Swiss system tournament, players are paired in every round and paired against opponents who have the same or simila... [more] | NLP2015-151 pp.53-56 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS (Joint) [detail] |
2015-06-23 11:10 |
Okinawa | Okinawa Institute of Science and Technology | Corpus and Topic Scalable Topic Model Soma Yokoi, Issei Sato, Hiroshi Nakagawa (UTokyo) IBISML2015-5 |
It is known that topic model with high dimensional topics improves IR performance like search engines and online adverti... [more] | IBISML2015-5 pp.27-31 |
| CAS, MSS, IPSJ-AL [detail] | 2014-11-21 14:10 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki island) | A Survey on Generation of Language-Family Tree by Applying Molecular Phylogenetic Approach Ren Wu (Yamaguchi JC.), Yuya Matsuura, Hiroshi Matsuno (Yamaguchi Univ.) CAS2014-104 MSS2014-68 |
In recent years, it has become popular to generate language-family trees of linguistics by applying the methods used in ... [more] | CAS2014-104 MSS2014-68 pp.147-152 |
| IN, IA (Joint) |
2012-12-13 18:20 |
Hiroshima | Hiroshima City Univ. | [Invited Talk]
Analysis of SNS Network using Precision Family-network Approximation Based on Multi-modal Nonlinear Markov-Transition Takeshi Ozeki (Sophia Univ.) IN2012-126 IA2012-64 |
Our motivation of communication network study is to find an abstractive network theory or methodology applicable to vari... [more] | IN2012-126 IA2012-64 pp.25-32(IN), pp.31-38(IA) |
| IBISML | 2011-06-21 10:00 |
Tokyo | Takeda Hall | Rare Event Sampling using Multicanonical Monte Carlo and its Application for Generating Surrogate Data Yukito Iba (ISM) IBISML2011-7 |
Developing efficient numerical techniques for rare event sampling isimportant in various fields.Markov chain Monte Carlo... [more] | IBISML2011-7 pp.43-50 |
| ITS, IE, ITE-AIT, ITE-HI, ITE-ME [detail] | 2011-02-21 16:25 |
Hokkaido | Hokkaido University | A note on accurate scene segmentation based on the MCMC method using object matching Yan Song, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) ITS2010-46 IE2010-121 |
This paper proposes an accurate scene segmentation method based on the Markov Chain Monte Carlo (MCMC) algorithm using o... [more] | ITS2010-46 IE2010-121 pp.131-135 |
| CAS, MSS, VLD, SIP | 2010-06-22 10:40 |
Hokkaido | Kitami Institute of Technology | A study on accurate scene segmentation based on the MCMC method utilizing video structures Yan Song, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) CAS2010-21 VLD2010-31 SIP2010-42 CST2010-21 |
This paper proposes a video scene segmentation method based on Markov Chain Monte Carlo(MCMC) method utilizing video str... [more] | CAS2010-21 VLD2010-31 SIP2010-42 CST2010-21 pp.115-120 |
| PRMU, IE, MI | 2009-05-28 16:15 |
Gifu | Gifu Univ. | Real-time estimation of human visual attention with MCMC-based particle filter Kouji Miyazato (NTT/Okinawa National College of Tech), Akisato Kimura (NTT), Shigeru Takagi (Okinawa National College of Tech), Junji Yamato (NTT) IE2009-25 PRMU2009-16 MI2009-16 |
This report proposes a new method for achieving a precise estimation of human visual attention with considerably less ex... [more] | IE2009-25 PRMU2009-16 MI2009-16 pp.83-88 |
| NC | 2007-07-25 10:30 |
Kyoto | Kyoto Univ. | On the Computation Approach of Learning Coefficients by Weighted Resolution of Singularities Takeshi Matsuda, Sumio Watanabe (Tokyo Inst. of Tech) NC2007-31 |
The learning machines which have singular Fisher information matrices are called singular statistical models. It says th... [more] | NC2007-31 pp.23-27 |
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