Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
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 |
NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-25 11:50 |
Tokushima |
Naruto University of Education |
Optimization of synaptic scaling rule, its implementation on modular spiking neural networks and analysis of its affects Takumi Shinkawa, Hideyuki Kato (Oita Univ.), Yoshitaka Ishikawa (FUN), Takuma Sumi, Hideaki Yamamoto (Tohoku Univ.), Yuichi Katori (FUN) NLP2023-107 MICT2023-62 MBE2023-53 |
In this study, to theoretically investigate the information processing mechanisms in the brain, we optimized synaptic sc... [more] |
NLP2023-107 MICT2023-62 MBE2023-53 pp.110-113 |
SCE |
2024-01-23 13:35 |
Tokyo |
Kikai-Shinko-Kaikan Bldg. (Primary: On-site, Secondary: Online) |
[Invited Talk]
Research on Novel Binary Neural Processing Elements Using Single Flux Quantum Circuits Zeyu Han, Zongyuan Li, Yamanashi Yuki, Yoshikawa Nobuyuki (Yokohama National Univ.) SCE2023-23 |
Superconducting convolutional neural networks, based on single flux quantum (SFQ) circuits, hold significant potential d... [more] |
SCE2023-23 pp.1-6 |
EMT, PN, MWPTHz, IEE-EMT [detail] |
2024-01-22 10:55 |
Kyoto |
Kyoto Univ. Yoshida Campus |
Transmission Characteristics Adaptation of a Microstrip Line Filters Using the Transformer Keiichiro Yamada, Takashi Kuroki, Naoki Miyata (TMCIT) PN2023-49 EMT2023-89 MWPTHz2023-77 |
This paper presents new technique for inverse model that can be proposed geometrical parameters of microwave circuit fro... [more] |
PN2023-49 EMT2023-89 MWPTHz2023-77 pp.11-16 |
SeMI |
2024-01-18 13:30 |
Yamanashi |
Raki House Kaiji |
Enhancing Human Skeleton Estimation with Multi-Frame mmWave Radar Point Cloud-based Method Xintong Shi, Tomoaki Ohtsuki (Keio Univ.) SeMI2023-52 |
Millimeter-Wave (mmWave) radar-based skeleton estimation has emerged as a focal point in the realm of human motion analy... [more] |
SeMI2023-52 pp.18-21 |