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 |