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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 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
 Results 1 - 20 of 37  /  [Next]  
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