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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, MBE (Joint) |
2024-03-12 14:45 |
Tokyo | The Univ. of Tokyo (Primary: On-site, Secondary: Online) |
Investigating the Effect of Skip Connection on Learning Dynamics in the Initial Learning Process of Deep Neural Networks Ryodo Yuge, Takashi Shinozaki (Kindai Univ.)  NC2023-59 |
We visualize the impact of skip connections, a key element in residual networks (ResNet), and visualize its impact on th... [more] | NC2023-59 p.94 |
| NC, MBE (Joint) |
2024-03-11 10:25 |
Tokyo | The Univ. of Tokyo (Primary: On-site, Secondary: Online) |
Potential of neural network for CT with divided cross sectional image using scattered X-ray Taiki Matsushita, Naohiro Toda (APU) MBE2023-68 |
In the X-ray CT(Computed Tomography) scattered X-rays have been removed by the detector grid. However several author hav... [more] | MBE2023-68 pp.1-4 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-03 09:00 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Analysis of the Impact of Different Resolutions and Datasets on the Architecture Searched with PC-DARTS Kaisei Hara (Nagaoka Univ. of Technology/AIST), Kazuki Hemmi (Univ. of Tsukuba/AIST), Masaki Onisi (AIST/Univ. of Tsukuba) PRMU2023-57 |
In deep learning, image resolution is crucial to improve accuracy and generalizability. However, the research on the spe... [more] | PRMU2023-57 pp.35-40 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-03 15:24 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
PRMU2023-62 |
(To be available after the conference date) [more] | PRMU2023-62 pp.64-69 |
| MI | 2024-03-03 14:30 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Invited Lecture]
Latest Research Trends 2023: Machine Learning for Medical Image Processing Fukashi Yamazaki (Canon) MI2023-48 |
In this paper, we overview the outlines of MICCAI 2023’s main conference sessions and satellite workshops. Several inter... [more] | MI2023-48 pp.53-55 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-04 09:00 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
PRMU2023-64 |
(To be available after the conference date) [more] | PRMU2023-64 pp.76-81 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-04 09:24 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
PRMU2023-66 |
(To be available after the conference date) [more] | PRMU2023-66 pp.88-93 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-04 11:16 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
PRMU2023-73 |
(To be available after the conference date) [more] | PRMU2023-73 pp.128-133 |
| EMM | 2024-03-02 16:20 |
Overseas | Day1:JEJU TECHNOPARK, Day2:JEJU Business Agency | [Fellow Memorial Lecture]
Application of associative memory models to watermarking models Masaki Kawamura (Yamaguchi Univ.) EMM2023-93 |
We proposed a new method called the associative watermarking method, which is an extension of the zero-watermarking meth... [more] | EMM2023-93 pp.23-27 |
| HCS | 2024-03-02 12:05 |
Shizuoka | Tokoha University(Shizuoka-Kusanagi Campus) | Estimation of willingness to participate in other's conversation by using deep learning of facial expression measurements Kohei Yamamoto, Jiro Okuda (Kyoto Sangyo Univ.) HCS2023-92 |
In recent years, there has been much interest in developing agents that can join conversations among multiple people and... [more] | HCS2023-92 pp.25-30 |
| AI | 2024-03-01 13:40 |
Aichi | Room0221, Bldg.2-C, Nagoya Institute of Technology | Applying Graph Neural Networks and Reinforcement Learning to the Multiple Depot-Multiple Traveling Salesman Problem Dongyeop Kim, Toshihiro Matsui (NITech) AI2023-39 |
In this study, we introduce a method combining Graph Neural Networks (GNN) and reinforcement learning for the Multiple D... [more] | AI2023-39 pp.13-18 |
| AI | 2024-03-01 14:40 |
Aichi | Room0221, Bldg.2-C, Nagoya Institute of Technology | Request span extraction from dialog with Heterogeneous Graph Attention Networks Naoki Mizumoto, Katsuhide Fujita (TUAT) AI2023-41 |
In this study, we formulate the problem of extracting user requests from the dialogue history as a ``span extraction pro... [more] | AI2023-41 pp.25-30 |
| DC | 2024-02-28 13:40 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Test Point Selection Method for Multi-Cycle BIST Using Deep Reinforcement Learning Kohei Shiotani, Tatsuya Nishikawa, Shaoqi Wei, Senling Wang, Hiroshi Kai, Yoshinobu Higami, Hiroshi Takahashi (Ehime Univ.) DC2023-98 |
Multi-cycle BIST is a test method that performs multiple captures for each scan pattern, proving effective in reducing t... [more] | DC2023-98 pp.23-28 |
| VLD, HWS, ICD | 2024-03-01 10:10 |
Okinawa | (Primary: On-site, Secondary: Online) |
Fault Detectable Convolutional Neural Network Circuits With Dual Modular Redundancy Based on Mixed-precision Quantization Yamato Saikawa, Yuta Owada, Yoichi Tomioka, Hiroshi Saito, Yukihide Kohira (UoA) VLD2023-122 HWS2023-82 ICD2023-111 |
In safety-critical edge AI systems, circuit failures caused by aging or cosmic ray can lead to serious accidents. Dual M... [more] | VLD2023-122 HWS2023-82 ICD2023-111 pp.119-124 |
| VLD, HWS, ICD | 2024-03-02 09:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
Countermeasure on AI Hardware against Adversarial Examples Kosuke Hamaguchi, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) VLD2023-134 HWS2023-94 ICD2023-123 |
The demand for edge AI, in which artificial intelligence (AI) is directly embedded in devices, is increasing, and the se... [more] | VLD2023-134 HWS2023-94 ICD2023-123 pp.184-189 |
| EID, ITE-IDY, IEE-EDD, SID-JC, IEIJ-SSL [detail] | 2024-01-25 13:15 |
Kyoto | (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Reproduction of changes in membrane potential of neurons by synaptic devices using memristors Kenta Yachida, Yoshiya Abe, Kazuki Sawai (Ryukoku Univ.), Tokiyoshi Matsuda (Kindai Univ./Ryukoku Univ.), Hidenori Kawanishi (Ryukoku Univ.), Mutsumi Kimura (Ryukoku Univ./NAIST) EID2023-4 |
We attempted to replicate the changes in the membrane potential of neurons using thin-film neuromorphic devices that int... [more] | EID2023-4 pp.9-12 |
| PRMU, MVE, VRSJ-SIG-MR, IPSJ-CVIM | 2024-01-26 15:46 |
Kanagawa | Keio Univ. (Hiyoshi Campus) | PRMU2023-48 |
In the realm of autonomous driving, end-to-end models (E2EDMs) have gained prominence due to their high predictive accur... [more] | PRMU2023-48 pp.46-49 |
| ICTSSL, CAS | 2024-01-25 11:45 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Comparison of transfer learning and fine tuning Ohata Shunsuke, Okazaki Hideaki (SIT) CAS2023-88 ICTSSL2023-41 |
This report examines the principal image recognition methods. First, we show the experimental results of image recogniti... [more] | CAS2023-88 ICTSSL2023-41 pp.31-33 |
| NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-24 10:00 |
Tokushima | Naruto University of Education | Hierarchical lossless compression of high dynamic range images using predictors based on cellular neural networks Seiya Kushi, Kazuki Nakashima, Hideharu Toda (Chukyo Univ.), Tsuyoshi Otake (Tamagawa Univ.), Hisashi Aomori (Chukyo Univ.) NLP2023-85 MICT2023-40 MBE2023-31 |
We have been developing a scalable lossless coding method using cellular neural networks (CNN) as predictors. This metho... [more] | NLP2023-85 MICT2023-40 MBE2023-31 pp.12-15 |
| NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-25 09:00 |
Tokushima | Naruto University of Education | The Relationship Between Metrics in the Latent Variable Space and Image Classification Performance Haruki Wakasa, Kenya Jin'no (Tokyo City Univ.) NLP2023-99 MICT2023-54 MBE2023-45 |
In recent years, models based on convolutional neural networks (CNNs) have exhibited high performance in image classific... [more] | NLP2023-99 MICT2023-54 MBE2023-45 pp.78-81 |
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