Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
IBISML |
2023-12-21 10:55 |
Tokyo |
National Institute of Informatics (Primary: On-site, Secondary: Online) |
On the benefits of Partial Stochastic Bayesian Neural Networks Koki Sato, Daniel Andrade (Hiroshima Univ.) IBISML2023-36 |
Bayesian neural networks (BNNs) can model uncertainty in the prediction results better than ordinary neural networks. Ho... [more] |
IBISML2023-36 pp.37-41 |
CQ, CS (Joint) |
2022-05-12 16:35 |
Fukui |
Fukui (Fuku Pref.) (Primary: On-site, Secondary: Online) |
Study on an Autonomous Adaptive Mechanism for Robustness of the User-Aware Resource Assignment against Demand Fluctuation Keita Tatebe, Yusuke Sakumoto (Kwansei Gakuin Univ.) CQ2022-10 |
The assignment problem on networks is a fundamental problem associated with various methods such as distributed computin... [more] |
CQ2022-10 pp.50-55 |
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 |
R |
2020-12-11 15:15 |
Online |
Online |
A Note on Variance-Based Sensitivity Analysis for Continuous-Time Markov Chains Based on Moment Approximation Jiahao Zhang (Hiroshima Univ.), Junjun Zheng (Ritsumeikan Univ.), Hiroyuki Okamura, Tadashi Dohi (Hiroshima Univ.) R2020-33 |
Sensitivity analysis plays a critical role in quantifying uncertainty in the design of computer systems. In particular, ... [more] |
R2020-33 pp.18-23 |
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 (Joint) |
2020-03-05 16:10 |
Tokyo |
University of Electro Communications (Cancelled but technical report was issued) |
Improvement of neuronal ensemble inference by Monte Carlo method and applying to real data Shun Kimura, Koujin Takeda (Ibaraki Univ.), Keisuke Ota (Riken) NC2019-101 |
In this work, we propose an improved inference algorithm for neuronal ensembles, which can classify neurons into ensembl... [more] |
NC2019-101 pp.149-154 |
IA, SITE, IPSJ-IOT [detail] |
2020-03-03 10:05 |
Online |
Online |
A Study on Autonomous Decentralized Allocation Method for Content Replicas in ICN Toshitaka Kashimoto, Yusuke Sakumoto (Kwansei Gakuin Univ) SITE2019-95 IA2019-73 |
The in-network caching is discussed as a method to reduce the delivery time of contents in ICNs. The efficiency of the ... [more] |
SITE2019-95 IA2019-73 pp.93-98 |
NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] |
2019-06-17 17:10 |
Okinawa |
Okinawa Institute of Science and Technology |
MCMC for Value-at-Risk estimation Igor Zavialov, Kazushi Ikeda (NAIST) NC2019-11 |
Value-at-Risk models (VaR) are powerful tools for financial risk management and are widely used by regulating authoritie... [more] |
NC2019-11 pp.41-44 |
R |
2018-05-25 15:30 |
Aichi |
Aichi Institute of Technology, Motoyama Campus |
Bayesian Interval Estimation of Optimal Software Release Time Based on a Discretized NHPP Model Shinji Inoue (Kansai Univ.), Shigeru Yamada (Tottori Univ.) R2018-4 |
We discuss an approach for obtaining interval estimation of optimal software release time which is derived by a discreti... [more] |
R2018-4 pp.19-24 |
CS, NS, IN, NV (Joint) |
2017-09-08 10:50 |
Miyagi |
Research Institute of Electrical Communication, Tohoku Univ. |
Performance Inference for Cooperative Spectrum Sensing with the k-out-of-N Rule: An MCMC-based Approach Sho Iizuka, Jun Kawahara, Shoji Kasahara (NAIST) NS2017-82 |
In the research of cognitive radio, Cooperative Spectrum Sensing (CSS) is proposed, in which the secondary users (SUs) f... [more] |
NS2017-82 pp.67-72 |
R |
2017-07-28 16:50 |
Hokkaido |
Wakkanai Sun Hotel |
Software Reliability Assessment Based on a Discretized Model by Bayes' Theory Shinji Inoue (Kansai Univ.), Shigeru Yamada (Tottori Univ.) R2017-23 |
We discuss an interval estimation approach for model parameters and software reliability assessment measures of a discre... [more] |
R2017-23 pp.55-60 |
IT |
2016-12-13 14:50 |
Gifu |
Takayama Green Hotel |
[Invited Talk]
Recent topics in Markov-chain Monte Carlo method Koji Hukushima (The Univ. of Tokyo) IT2016-43 |
Monte Carlo (MC) methods have been applied to a large class of problems as a
numerical tool for sampling from a high-d... [more] |
IT2016-43 pp.9-14 |
VLD, CAS, MSS, SIP |
2016-06-16 10:30 |
Aomori |
Hirosaki Shiritsu Kanko-kan |
On random test pattern generation algorithm considering signal transition activities Yusuke Matsunaga (Kyushu Univ.) CAS2016-4 VLD2016-10 SIP2016-38 MSS2016-4 |
This paper presents a test pattern generation method with considering
signal transition activities using Markov chain... [more] |
CAS2016-4 VLD2016-10 SIP2016-38 MSS2016-4 pp.19-22 |
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 |
MI |
2015-09-08 14:00 |
Tokyo |
Univ. of Electro-communications |
Feature Selection for Diffuse Lung Disease using MCMC Method Makoto Koiwai (UEC), Maki Isogai (Info Techno Asahi), Hayaru Shouno (UEC), Shoji Kido (Yamaguchi Univ.) MI2015-52 |
(To be available after the conference date) [more] |
MI2015-52 pp.19-24 |
NC, IPSJ-BIO, IBISML, IPSJ-MPS (Joint) [detail] |
2015-06-24 11:25 |
Okinawa |
Okinawa Institute of Science and Technology |
Repulsive parallel MCMC algorithm for discovering diverse motifs from large sequence sets. Hisaki Ikebata (SOKENDAI), Ryo Yoshida (ISM) IBISML2015-19 |
It is important to predict TFBSs (transcription factor binding sites) for the elucidation of the mechanism in gene regul... [more] |
IBISML2015-19 pp.143-147 |
NS, IN (Joint) |
2015-03-03 10:30 |
Okinawa |
Okinawa Convention Center |
Adapting the Autonomous Decentralized Control Based on MCMC against Environmental Fluctuation Masaya Yokota, Yusuke Sakumoto, Masaki Aida (TMU) IN2014-149 |
Autonomous Decentralized Control~(ADC) is being actively discussed for realizing control of large-scale and wide area ne... [more] |
IN2014-149 pp.169-174 |
MBE, NC (Joint) |
2014-11-21 11:50 |
Miyagi |
Tohoku University |
Hyper-parameter estimation for compressive sensing with a Bernoulli-Gauss prior distribution Toshiyuki Watanabe, Jun-ichi Inoue (Hokkaido Univ.) NC2014-28 |
Compressive sensing is a theory that estimates sparse
information signals which has few non-zero elements
from less ... [more] |
NC2014-28 pp.15-20 |
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 |
IBISML |
2014-11-17 17:00 |
Aichi |
Nagoya Univ. |
[Poster Presentation]
Feature Extraction for Image Classification using Restricted Boltzmann Machines Reiki Suda, Koujin Takeda (Ibaraki Univ.) IBISML2014-36 |
Learning restricted Boltzmann machines (RBMs) for high-dimensional data using maximum likelihood estimation had been fac... [more] |
IBISML2014-36 pp.9-15 |