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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CQ | 2024-06-27 13:00 |
Okinawa | (Primary: On-site, Secondary: Online) |
[Invited Lecture]
In-Network Inference Assisted by eBPF, XDP, and AF_XDP Takanori Hara (NAIST), Masahiro Sasabe (Kansai Univ.) |
(To be available after the conference date) [more] | |
| IBISML, NC, IPSJ-BIO, IPSJ-MPS [detail] | 2024-06-20 14:20 |
Okinawa | OIST | Anomaly Detection in the Frequency Domain with Statistical Reliability Akifumi Yamada, Kouichi Taji (Nagoya Univ.), Ichiro Takeuchi (Nagoya Univ./RIKEN) |
(To be available after the conference date) [more] | |
| IBISML, NC, IPSJ-BIO, IPSJ-MPS [detail] | 2024-06-20 15:00 |
Okinawa | OIST | Selective Inference for Reliability Quantification of k-Nearest Neighbor Anomoaly Detection Mizuki Niihori, Akihumi Yamada (Nagoya Univ.), Masaya Ikuta (NITech), Kouichi Taji, Ichiro Takeuchi (Nagoya Univ.) |
(To be available after the conference date) [more] | |
| IBISML, NC, IPSJ-BIO, IPSJ-MPS [detail] | 2024-06-20 15:25 |
Okinawa | OIST | Selective Inference for Anomaly Detection using Diffusion Models Teruyuki Katsuoka, Tomohiro Shiraishi (Nagoya Univ.), Daiki Miwa (NITech), Vo Nguyen Le Duy (RIKEN), Ichiro Takeuchi (Nagoya Univ./RIKEN) |
(To be available after the conference date) [more] | |
| RCS | 2024-06-20 10:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
A Study on Low-Complexity LLR Calculation Based on Bilinear Inference for MIllimeter-Wave MIMO Systems Using DA-QSMmmWave MIMO Detection via Belief Propagation for DA-QSM Keigo Masuoka, Takumi Takahashi (Osaka Univ.), Shinkuke Ibi (Doshisha Univ.), Hideki Ochiai (Osaka Univ.) |
(To be available after the conference date) [more] | |
| ICSS, IPSJ-SPT | 2024-03-22 11:20 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
Evaluation of Feature Inference Risk from Explainable AI metrics LIME and Shapley Values Ryotaro Toma, Hiroaki Kikuchi (Meiji Univ.)  ICSS2023-88 |
Explainability has gained attention to ensure fairness and transparency in machine learning models, providing users with... [more] | ICSS2023-88 pp.137-144 |
| KBSE | 2024-03-15 14:50 |
Okinawa | Okinawa Prefectual General Welfare Center (Primary: On-site, Secondary: Online) |
Learning data creation support tool for learning program defects using images Kazuhiko Ogawa, Takako Nakatani (OUJ) KBSE2023-89 |
We have developed CNN-BI system that learns and infers defects from the images of programs. This paper introduces a tool... [more] | KBSE2023-89 pp.132-137 |
| RCC, ISEC, IT, WBS | 2024-03-13 - 2024-03-14 |
Osaka | Osaka Univ. (Suita Campus) | Comparison of Scale Parameter Dependence of Estimation Performance in Sparse Bayesian Linear Regression Model with Variance Gamma Prior Distribution and t-Prior Distribution Kazuaki Murayama (UEC) IT2023-135 ISEC2023-134 WBS2023-123 RCC2023-117 |
In the sparse estimation with linear regression model, the variance gamma distribution and t-distribution can be used as... [more] | IT2023-135 ISEC2023-134 WBS2023-123 RCC2023-117 pp.374-379 |
| RCS, SR, SRW (Joint) |
2024-03-13 16:40 |
Tokyo | The University of Tokyo (Hongo Campus), and online (Primary: On-site, Secondary: Online) |
A Plug-and-Play Module for Enhancing Fault-Tolerant Distributed Inference Based on Gaussian Dropout Hou Zhangcheng, Ohtsuki Tomoaki (KU) RCS2023-267 |
Distributed inference (DI) in the Internet of Things (IoT) is becoming increasingly important as the demand for AI appli... [more] | RCS2023-267 pp.77-82 |
| MI | 2024-03-03 09:05 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Generation of Counterfactual Pathology Images of Malignant Lymphoma using Diffusion Models Ryoichi Koga, Tatsuya Yokota (NIT), Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi (Kurume Univ.), Noriaki Hashimoto (RIKEN), Ichiro Takeuchi (Nagoya Univ.), Hidekata Hontani (NIT) MI2023-30 |
Malignant lymphoma has more than 70 subtypes. In the pathological diagnosis, a pathological image is observed to identif... [more] | MI2023-30 pp.1-2 |
| NS, IN (Joint) |
2024-02-29 10:10 |
Okinawa | Okinawa Convention Center | Multimodal Object Recognition Method Using Bayesian Attractor Model For 3D Point Clouds and RGB Images Haruhito Ando, Daichi Kominami, Ryoga Seki, Masayuki Murata, Hideyuki Shimonishi (Osaka Univ.) NS2023-192 |
Beyond 5G/6G, technology is driving the development of digital twins. In recent years, the amount of information that ca... [more] | NS2023-192 pp.119-124 |
| ICTSSL, CAS | 2024-01-25 10:20 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Using Deep Learning to Recognize and Determine the State of Snow on LED Traffic Signals Regardless of the Outside Environment and Its Application Megumi Matsuzaka, Tsubasa Saito, Yuichi Sato (Akita Univ.) CAS2023-84 ICTSSL2023-37 |
Currently, the use of LEDs in traffic signals is being promoted for various advantages. However, due to the characterist... [more] | CAS2023-84 ICTSSL2023-37 pp.11-16 |
| SIP, IT, RCS | 2024-01-19 10:20 |
Miyagi | (Primary: On-site, Secondary: Online) |
Coordinated Multi-Point with Hierarchical Active Inference of Position and Propagation Channel Tatsuya Otoshi, Masayuki Murata (Osaka Univ.) IT2023-63 SIP2023-96 RCS2023-238 |
This study focuses on cooperative beamforming among base stations in wireless communication technology and proposes a ne... [more] | IT2023-63 SIP2023-96 RCS2023-238 pp.181-186 |
| 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 |
| KBSE, SC | 2023-11-18 15:10 |
Miyagi | Sento Kaikan | Research to improve the accuracy of inferring program defects using deep learning Kazuhiko Ogawa, Takako Nakatani (OUJ) KBSE2023-50 SC2023-33 |
Experienced developers seem to be able to identify defects in programs created by beginners at a glance. We applied sup... [more] |
KBSE2023-50 SC2023-33 pp.93-98 |
| AI | 2023-09-12 15:35 |
Hokkaido | Visualization of Inference Process of Autonomous System Misaki Kinoshita (Nara Women's Univ.) AI2023-5 |
Autonomous agent systems (hereafter referred to as "autonomous systems"), which do not act according to human instructio... [more] | AI2023-5 pp.25-32 |
|
| ET | 2023-07-14 13:35 |
Hokkaido | Muroran Institute of Technology / Online (Primary: On-site, Secondary: Online) |
Development of a learning support system of inference rules for solving Number Place puzzles for beginners Haruki Honaga, Hisayoshi Kunimune (Chiba Inst. of Tech.) ET2023-10 |
``Number Place'', also known as Sudoku, is a type of pencil puzzles in which the numbers 1 to 9 are to be filled on 9 x ... [more] | ET2023-10 pp.7-10 |
| SeMI, RCS, RCC, NS, SR (Joint) |
2023-07-13 13:50 |
Osaka | Osaka University Nakanoshima Center + Online (Primary: On-site, Secondary: Online) |
A Study on Bayesian Receiver Design for Uplink MU-MIMO Systems with Carrier Frequency Offsets Kenta Ito, Takumi Takahashi, Koji Igarashi (Osaka Univ.), Koji Ishibashi (UEC), Shinsuke Ibi (Doshisha Univ.) RCS2023-93 |
This paper proposes a novel Bayesian receiver design to realize high-accuracy data detection under observations subject ... [more] | RCS2023-93 pp.70-75 |
| MSS, CAS, SIP, VLD | 2023-07-06 14:40 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Convergence Acceleration of Particle-based Variational Inference by Deep Unfolding Yuya Kawamura, Satoshi Takabe (Tokyo Tech) CAS2023-8 VLD2023-8 SIP2023-24 MSS2023-8 |
Stein Variational Gradient Descent(SVGD) is a prominent particle-based variational inference method used for estimating ... [more] | CAS2023-8 VLD2023-8 SIP2023-24 MSS2023-8 pp.37-42 |
| MI | 2023-07-03 10:00 |
Miyagi | Tohoku Univ. Sakura Hall | HCP data processing by hybrid approach for parameter inference in free water imaging model of diffusion MRI
-- comparison of diffusion tensor computation methods for datasets without b0 image -- Yoshitaka Masutani (Tohoku Univ.), Keigo Yamazaki, Wataru Uchida, Koji Kamagata (Juntendo Univ.), Koh Sasaki (HHC), Shigeki Aoki (Juntendo Univ.) MI2023-7 |
We have proposed a hybrid approach combining synthetic Q-space learning and conventional fitting for parameter estimatio... [more] | MI2023-7 pp.1-2 |
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