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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2024-03-04 10:46 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Automated musculoskeletal segmentation of torso CT images Sanaa Amina Gourine, Mazen Soufi, Yoshito Otake (NAIST), Yuto Masaki (NAIST-PSP Corporation), Yoko Murakami, Yukihiro Nagatani, Yoshiyuki Watanabe (Shiga Univ), Keisuke Uemura (Osaka Univ), Masaki Takao (Ehime Univ), Nobuhiko Sugano (Osaka Univ), Yoshinobu Sato (NAIST)  MI2023-70 |
Musculoskeletal segmentation (MSK) in CT is helpful for several applications, including body composition analysis, biome... [more] | MI2023-70 pp.122-126 |
| 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, IN (Joint) |
2023-08-03 10:51 |
Hokkaido | Banya-no-yu | Preprocessing Study for Detecting Out-of-Distribution Image Data with Bayesian Neural Network Koki Minagawa, Taisei Saito, Sena Kojima, Tetsuya Asai (Hokkaido Univ.) CCS2023-19 |
Out-of-distribution (OOD) data ditection is a critical issue in ensuring the security of machine learning models. In th... [more] |
CCS2023-19 pp.13-18 |
| 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 |
| ET | 2023-03-14 13:20 |
Tokushima | Tokushima University (Primary: On-site, Secondary: Online) |
Estimation of Learning Methods from Tools for Monitoring Learning Process Kento Kuwajima, Atsushi Ashida, Tomoko Kojiri (Kansai Univ.) ET2022-69 |
In the future, with the recent development of robot technology, teacher robots will be introduced in the educational set... [more] | ET2022-69 pp.57-64 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 16:55 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Upper bound of real log canonical threshold based on linear programming problem for the multi-indexes of a polynomial Joe Hirose (Tokyo Tech) PRMU2022-125 IBISML2022-132 |
A real log canonical threshold (RLCT) is an invariant which gives a Bayesian generalization error. While a strict value ... [more] | PRMU2022-125 IBISML2022-132 pp.363-370 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-27 14:25 |
Okinawa | (Primary: On-site, Secondary: Online) |
A Bagging Method to Improve the Accuracy of Gaussian Process Regression for Neural Architecture Search Rion Hada, Masao Okita, Fumihiko Ino (Osaka Univ.) NC2022-2 IBISML2022-2 |
The goal of this study is to improve performance estimation for neural network architectures in neural architecture sear... [more] | NC2022-2 IBISML2022-2 pp.6-13 |
| 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 |
| NS, IN (Joint) |
2022-03-11 11:40 |
Online | Online | A Study on Bayesian Spatial and Temporal Modeling Approach to Environmental Feature Inference Using Driving Signals From Vehicles Yukio Ogawa (Muroran-IT), Go Hasegawa (Tohoku Univ.), Masayuki Murata (Osaka Univ.) IN2021-43 |
Connected vehicles become an ambient sensing platform, as a number of different signals that they record become availabl... [more] | IN2021-43 pp.73-78 |
| CQ, IMQ, MVE, IE (Joint) [detail] |
2022-03-10 18:30 |
Online | Online (Zoom) | Implementation and evaluation of an object recognition method for Digital Twin using cognitive mechanism of the Brain Kaito Kubo, Ryoga Seki, Daichi Kominiami, Hideyuki Shimonishi, Masayuki Murata (Osaka Univ.), Masaya Fujiwaka (NEC) CQ2021-125 |
It is desired to construct a digital twin that can sense objects such as people and objects in the real world and repres... [more] | CQ2021-125 pp.136-141 |
| CQ, CBE (Joint) |
2022-01-27 16:05 |
Ishikawa | Kanazawa（Ishikawa Pref.） (Primary: On-site, Secondary: Online) |
Proposal and evaluation of 3D-point object estimation method based on probability space representation Hiroaki Sato, Shin'ichi Arakawa, Masayuki Murata (Osaka Univ.) CQ2021-83 |
New network services are expected to emerge using real spatial information in remote areas. For the advancement of servi... [more] | CQ2021-83 pp.39-44 |
| 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 |
| ICM | 2021-07-16 10:00 |
Online | Online | Event Correlation Method with Bayesian Network Atsushi Takada, Naoki Hayashi, Ryosuke Sato, Toshihiko Seki, Kyoko Yamagoe (NTT) ICM2021-15 |
Nowadays, research has been conducted to automate the operation of IT services using AI and orchestrator. In the operati... [more] | ICM2021-15 pp.28-33 |
| ICM | 2021-03-19 14:45 |
Online | Online | Method study and proposal of workflow engine for automation using AI Ryosuke Sato, Mizuto Nakamura, Atsushi Takada, Toshihiko Seki, Kyoko Yamagoe (NTT) ICM2020-76 |
The introduction of AI is being considered in NW operation. It is expected to automate atypical failures involving compl... [more] | ICM2020-76 pp.92-97 |
| 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 |
| CQ, CBE (Joint) |
2020-01-17 09:40 |
Tokyo | NHK Science & Technology Research Laboratories | Bayesian channel selection method for LoRaWAN under unpredictable wireless channel fluctuations Daichi Kominami (Osaka Univ.), Yohei Hasegawa, Kosuke Nogami, Hideyuki Shimonishi (NEC), Masayuki Murata (Osaka Univ.) CQ2019-122 |
Internet of Things (IoT) become a common term used in society. LPWA technology is attracting attention as one of its ele... [more] | CQ2019-122 pp.83-88 |
| IBISML | 2020-01-09 13:25 |
Tokyo | ISM | Real Log Canonical Threshold of Three Layered Neural Network with Swish Activation Function Raiki Tanaka, Sumio Watanabe (Tokyo Tech) IBISML2019-19 |
In neural network learning, it is known that selection of activation function effects generalization performance. Althou... [more] | IBISML2019-19 pp.9-15 |
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