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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 33  /  [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
CQ 2020-09-03
11:20
Osaka Osaka University Nakanoshima Center
(Primary: On-site, Secondary: Online)
The Effect of Scale-Free Structure of Network on Autonomous Decentralized Allocation Control of Content Replicas
Toshitaka Kashimoto, Yusuke Sakumoto (Kwansei Univ.) CQ2020-36
Information centric network (ICN) aims to realize efficient content delivery by using in-network caching. Some studies ... [more] CQ2020-36
pp.9-14
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
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
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
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
IBISML 2014-03-06
13:50
Nara Nara Women's University Finding scale-free networks of Gaussian graphical models by sampling
Shota Shikita, Osamu Maruyama (Kyushu Univ.) IBISML2013-69
The problem of learning the structure of a Gaussian graphical model is to infer the graph representing the relationship ... [more] IBISML2013-69
pp.15-22
MBE, NC
(Joint)
2013-03-15
10:15
Tokyo Tamagawa University Bayesian inference for GTM using non-stationary Gaussian process
Nobuhiko Yamaguchi (Saga Univ.) NC2012-168
Generative Topographic Mapping (GTM) is a nonlinear topographically preserving mapping from latent to data space introdu... [more] NC2012-168
pp.197-202
IBISML 2012-11-08
15:00
Tokyo Bunkyo School Building, Tokyo Campus, Tsukuba Univ. An Efficient Sampling Algorithm for Bayesian Variable Selection
Takamitsu Araki, Kazushi Ikeda (NAIST) IBISML2012-75
In Bayesian variable selection, a Gibbs variable selection (GVS) is one of the most famous sampling algorithms, and has ... [more] IBISML2012-75
pp.291-295
MBE, NC
(Joint)
2012-03-15
13:20
Tokyo Tamagawa University Time Series Alignment with Gaussian Process Priors
Shinji Akimoto, Nobuo Suematsu, Akira Hayashi, Kazunori Iwata (Hiroshima City Univ.) NC2011-163
We propose a nonparametric Bayesian approach to time series alignment. Given a set of time series data, we can sometimes... [more] NC2011-163
pp.245-250
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