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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 57  /  [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
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
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