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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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 |
| SeMI, SeMI (Joint) |
2023-01-19 14:00 |
Tokushima | Naruto grand hotel (Primary: On-site, Secondary: Online) |
[Short Paper]
A Study of Model Training Method based on Federated Learning for Distributed Inference with Split Computing Yutaro Horikawa, Takayuki Nishio (Tokyo Tech) SeMI2022-76 |
SC (Split computing) is a distributed inference method for load balancing and latency reduction, which splits a neural n... [more] | SeMI2022-76 pp.25-27 |
| IT, ISEC, RCC, WBS | 2022-03-11 14:55 |
Online | Online | On Strong Converse Theorem for Distributed Hypothesis Testing Yasutada Oohama (UEC) IT2021-122 ISEC2021-87 WBS2021-90 RCC2021-97 |
In this study, we consider a communication system in which data generated at two points with correlation is separatley... [more] |
IT2021-122 ISEC2021-87 WBS2021-90 RCC2021-97 pp.228-233 |
| VLD, HWS [detail] | 2022-03-07 13:40 |
Online | Online | [Memorial Lecture]
DistriHD: A Memory Efficient Distributed Binary Hyperdimensional Computing Architecture for Image Classification Dehua Liang, Jun Shiomi, Noriyuki Miura (Osaka Univ.), Hiromitsu Awano (Kyoto Univ.) VLD2021-84 HWS2021-61 |
Hyper-Dimensional (HD) computing is a brain-inspired learning approach for efficient and fast learning on today’s embedd... [more] | VLD2021-84 HWS2021-61 p.44 |
| SeMI | 2022-01-20 15:10 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
A Study of Joint Control of Machine Learning Model and Wireless LAN Parameters in Split inference by Reinforcement Learning Kojin Yorita (Tokyo Tech.), Sohei Itahara (Kyoto Univ.), Takayuki Nishio (Tokyo Tech.), Daiki Yoda, Toshihisa Nabetani (Toshiba) SeMI2021-66 |
Distributed inference (DI) enables machine learning (ML) inference with a deep neural network on resource-constrained de... [more] | SeMI2021-66 pp.51-54 |
| KBSE | 2010-01-26 10:00 |
Tokyo | Univ. of Tsukuba, Tokyo | An Approach for Developing Distributed Web Inference Engines and Its Applications Yoshiaki Kubota, Ning Zhong (Maebashi Inst. of Tech.) KBSE2009-53 |
In this paper, we propose a model and system of distributed Web inference engines with the ability of combining rule-b... [more] |
KBSE2009-53 pp.29-34 |
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