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 |
VLD, DC, RECONF, ICD, IPSJ-SLDM [detail] |
2023-11-17 14:50 |
Kumamoto |
Civic Auditorium Sears Home Yume Hall (Primary: On-site, Secondary: Online) |
Hardware Compression Method Applying Bernoulli Approximation for Bayesian Neural Networks Taisei Saito, Kota Ando, Tetsuya Asai (Hokkaido Univ.) VLD2023-73 ICD2023-81 DC2023-80 RECONF2023-76 |
This study focuses on efficiently lightweighting Bayesian deep learning algorithms and implementing them on FPGA. It com... [more] |
VLD2023-73 ICD2023-81 DC2023-80 RECONF2023-76 pp.221-226 |
RECONF |
2023-08-04 14:55 |
Hokkaido |
Hakodate Arena (Primary: On-site, Secondary: Online) |
An Elastic FPGA-based Accelerator for Bayesian Network Structure Learning Ryota Miyagi (The Univ. of Tokyo), Ryota Yasudo (Kyoto Univ.), Kentaro Sano (RIKEN), Hideki Takase (The Univ. of Tokyo) RECONF2023-15 |
A Bayesian network is a powerful model for representing knowledge involving uncertainty within discrete random variables... [more] |
RECONF2023-15 pp.7-12 |
CCS |
2023-03-26 13:15 |
Hokkaido |
RUSUTSU RESORT |
Classification performance evaluation of untrained and trained data in Bayesian neural network and CNN ensemble Koki Minagawa, Taisei Saito, Sena Kojima, Tetsuya Asai (Hokkaido Univ.) CCS2022-71 |
The ditection of untrained (Out-of-Distribution; OOD) data is one of the problems in neural networks.
In this study, we... [more] |
CCS2022-71 pp.48-53 |
NS, IN (Joint) |
2022-03-11 10:40 |
Online |
Online |
Measurement Route Design Using Bayesian Optimization for Degraded Area Detection in Ultra-dense Networks Kotaro Matsuda, Hiroki Ikeuchi, Yousuke Takahashi, Akio Watanabe (NTT) IN2021-40 |
In ultra-dense wireless networks after 5G/6G, communication degradation is expected to increase. On the other hand, the ... [more] |
IN2021-40 pp.55-60 |
RECONF |
2021-09-10 15:00 |
Online |
Online |
Parallel Calculation of Local Scores in Bayesian Network Structure Learning using FPGA Ryota Miyagi (Kyoto Univ.), Hideki Takase (U. Tokyo/JST) RECONF2021-22 |
Bayesian network (BN) is a directed acyclic graph that represents relationships among variables in data sets. Because le... [more] |
RECONF2021-22 pp.30-35 |
RECONF |
2020-05-28 15:15 |
Online |
Online |
RECONF2020-7 |
A Bayesian network is one of the graphical models that represent the causality or correlation of multiple observed pheno... [more] |
RECONF2020-7 pp.37-42 |
NS, IN (Joint) |
2020-03-06 14:00 |
Okinawa |
Royal Hotel Okinawa Zanpa-Misaki (Cancelled but technical report was issued) |
Evaluation of Network Resource Allocation Based on Monitored Traffic Condition inspired by the Cognitive Process of the Human Brain Semin An, Yuichi Ohsita, Masayuki Murata (Osaka Univ.) IN2019-135 |
Many kinds of services have been provided through networks.
Traffic from such services should be accommodated so as to ... [more] |
IN2019-135 pp.339-344 |
NC, MBE |
2019-12-06 17:20 |
Aichi |
Toyohashi Tech |
Regularization Term of WRH Type Used with Moore-Penrose Inverse for Optimizing Neural Networks Yoshifusa Ito (FHU), Hiroyuki Izumi (AGU), Cidambi Srinivasan (UK) MBE2019-60 NC2019-51 |
Weigend et al. proposed an algorithm for optimizing neural networks, which suppressed the notorious over-tting. They at... [more] |
MBE2019-60 NC2019-51 pp.89-94 |
PN |
2019-03-14 13:15 |
Okinawa |
Miyako Island Hirara Port Terminal Bldg. |
Failure Probability Estimation based on Observation Information of Individual Network Equipment Masahiro Matsuno, Shu Sekigawa (Keio Univ.), Eiji Ooki (Kyoto Univ.), Satoru Okamoto, Naoaki Yamanaka (Keio Univ.) PN2018-83 |
As the communication traffic increases due to the spread of IoT (Internet of Things) and the progress of
fifth generati... [more] |
PN2018-83 pp.1-7 |
CCS |
2018-11-23 14:55 |
Hyogo |
Kobe Univ. |
Hypernetwork-based Implicit Posterior Estimation of CNN Kenya Ukai, Takashi Matsubara, Kuniaki Uehara (Kobe Univ.) CCS2018-45 |
Deep neural networks have a rich ability to learn complex representations and achieved remarkable results in various tas... [more] |
CCS2018-45 pp.67-72 |
NS |
2018-10-19 10:40 |
Kyoto |
Kyoto Kyoiku Bunka Center |
[Invited Lecture]
Study of Internet Traffic Classification Based on Naive Bayesian Method Yuanzhi Shao (UEC), Ved P. Kafle (NICT) NS2018-127 |
The traditional port- and payload-based traffic detection and classification technique may not meet the security and QoS... [more] |
NS2018-127 pp.111-116 |
NS |
2018-04-19 13:25 |
Fukuoka |
Fukuoka Univ. |
Channel assignment for LPWA networks inspired by perceptual decision-making of human brain Daichi Kominami (Osaka Univ.), Kazuya Suzuki, Yohei Hasegawa, Hideyuki Shimonishi (NEC), Masayuki Murata (Osaka Univ.) NS2018-2 |
Low power wide area (LPWA) technology that realizes low-power-consumption and wide-area communication is rapidly spreadi... [more] |
NS2018-2 pp.7-12 |
PN |
2018-03-05 14:55 |
Kagoshima |
Minami Tanemachi Shoko Kaikan |
[Invited Lecture]
Virtual Network Reconfiguration Based on Bayesian Attractor Selection Model for Optical Networks Toshihiko Ohba, Shin'ichi Arakawa, Masayuki Murata (Osaka Univ.) PN2017-96 |
One of approaches to accommodating traffic demand on an optical network is to configure a virtual network (VN), and reco... [more] |
PN2017-96 pp.31-38 |
NS, IN (Joint) |
2018-03-01 09:20 |
Miyazaki |
Phoenix Seagaia Resort |
Traffic engineering using incomplete information for cellular networks in case of disaster Kodai Satake, Yuichi Ohsita (Osaka Univ.), Keisuke Ishibashi, Yoko Hoshiai (NTT), Masayuki Murata (Osaka Univ.) IN2017-94 |
In case of disaster, traffic may increase and people move in the different way from the normal behavior, which may cause... [more] |
IN2017-94 pp.27-32 |
IBISML |
2017-11-09 13:00 |
Tokyo |
Univ. of Tokyo |
[Poster Presentation]
Learning huge Bayesian networks using RAI algorithm based on Bayes factor Kazuki Natori, Masaki Uto, Maomi Ueno (UEC) IBISML2017-58 |
``Learning Bayesian networks'' has NP-hard problem. The state-of-the-arts method of learning Bayesian networks cannot le... [more] |
IBISML2017-58 pp.177-184 |
CQ |
2017-07-27 12:05 |
Hyogo |
Kobe University |
Time series analysis of failure rates of equipments for telecommunication networks.
-- State space model using Bayesian inference -- Hiroyuki Funakoshi (NTT) CQ2017-35 |
The author has been analyzed the failure rate of telecommunication network equipments by time series analysis using ARIM... [more] |
CQ2017-35 pp.37-42 |
SC |
2017-03-10 15:45 |
Tokyo |
National Institute of Informatics |
Probabilistic Inference of Customer States Using Statistical Open Data and Bayesian Networks Hiroaki Nakamura, Michiharu Kudo, Hironori Takeuchi (IBM Japan) SC2016-35 |
Enterprises need to provide services specialized for each customer in a timely manner, and for that purpose, they rely o... [more] |
SC2016-35 pp.39-44 |
NS, IN (Joint) |
2017-03-03 13:40 |
Okinawa |
OKINAWA ZANPAMISAKI ROYAL HOTEL |
QoE enhancement for video streaming based on a human perceptual mechanism Daichi Kominami (Osaka Univ.), Takanori Iwai, Hideyuki Shimonishi (NEC), Masayuki Murata (Osaka Univ.) NS2016-221 |
In the near future, we will be able to access to all sorts of information via information networks. Then, it is desired ... [more] |
NS2016-221 pp.365-370 |
PN |
2016-11-17 15:05 |
Saitama |
KDDI Research, Inc. |
A Bayesian-based Virtual Network Reconfiguration in Elastic Optical Path Networks Toshihiko Ohba, Shin'ichi Arakawa, Masayuki Murata (Osaka Univ.) PN2016-33 |
A typical approach for constructing/reconfiguring a virtual network (VN) is to design an optimal topology and the amount... [more] |
PN2016-33 pp.45-50 |