IEICE Technical Committee Submission System
Conference Schedule
Online Proceedings
[Sign in]
Tech. Rep. Archives
    [Japanese] / [English] 
( Committee/Place/Topics  ) --Press->
 
( Paper Keywords:  /  Column:Title Auth. Affi. Abst. Keyword ) --Press->

All Technical Committee Conferences  (Searched in: All Years)

Search Results: Conference Papers
 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 9 of 9  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
IN, CCS
(Joint)
2021-08-05
14:25
Online Online Digital Implement of 3-layered Neural Networks with Stochastic Activation, Shunting Inhibition, and a Dual-rail Backpropagation
Yoshiaki Sasaki, Seiya Muramatsu, Kohei Nishida, Megumi Akai-Kasaya, Tetsuya Asai (Hokkaido Univ.) CCS2021-16
Stochastic computing (SC) is an arithmetic technique that enables various operations to be performed with a small number... [more] CCS2021-16
pp.7-13
MBE, NC, NLP, CAS
(Joint) [detail]
2020-10-29
16:10
Online Online Numerical research on effects of quantization in SNN learned by backpropagation
Yumi Watanabe, Jun Ohkubo (Saitama Univ.) NC2020-14
There are many studies to quantize the parameters of neural networks. For example, while there are methods of quantizing... [more] NC2020-14
pp.29-33
NC, MBE
(Joint)
2020-03-05
10:20
Tokyo University of Electro Communications
(Cancelled but technical report was issued)
An extension of the H_infinity learning to deep neural networks
Yasuhiro Sugawara, Kiyoshi Nishiyama (Iwate University) NC2019-92
In recent years, deep neural networks have achieved remarkable research results. In this study, we propose a method to e... [more] NC2019-92
pp.95-100
NC, MBE
(Joint)
2019-03-05
09:30
Tokyo University of Electro Communications Novel Backpropagation Algorithm Considering Energy
Rintaro Kanada, Masafumi Hagiwara (Keio Univ.) NC2018-63
In this paper, we propose a novel backpropagation(BP) algorithm considering energy. Neural network (NN) can be classifie... [more] NC2018-63
pp.105-110
MBE, NC
(Joint)
2018-03-14
15:30
Tokyo Kikai-Shinko-Kaikan Bldg. Gradually Stacking Neural Network
Shunya Sasaki, Masafumi Hagiwara (Keio Univ) NC2017-97
In this paper, we propose a neural network with multiple layers in a stepwise manner. Neural networks (NNs) become more ... [more] NC2017-97
pp.175-180
PRMU, IBISML, IPSJ-CVIM [detail] 2014-09-02
15:45
Ibaraki   Sampling Learning Algorithm by Oracle Distribution
Sho Sonoda, Noboru Murata (Waseda Univ.) PRMU2014-52 IBISML2014-33
A new sampling learning algorithm for neural networks is proposed. Based on the integral representation of neural networ... [more] PRMU2014-52 IBISML2014-33
pp.137-142
CS, OCS
(Joint)
2012-01-26
13:40
Mie ISESHI-KANKOUBUNKAKAIKAN Fractionally-Spaced Equalizer Based on High-Order Statistics in Nonlinear Fiber Optics
Toshiaki Koike-Akino, Chunjie Duan, Kieran Parsons, Keisuke Kojima (MERL), Tsuyoshi Yoshida, Takashi Sugihara, Takashi Mizuochi (ITC MELCO) OCS2011-108
Fiber nonlinearity has become a major limiting factor to realize ultra-high-speed optical communications. We propose a f... [more] OCS2011-108
pp.17-22
EMD 2010-11-12
14:15
Overseas Xi'an Jiaotong University On a Contact Failure Prediction and Reliability of Electrical Contacts
Zhiling Yu, Takahiro Ueno, Kenya Jin'no (Nippon Inst. of Tech.) EMD2010-115
The contact devices are widely used in electrical circuits, and very important. For this reason, they are required high ... [more] EMD2010-115
pp.201-204
NC 2007-05-21
10:25
Kanagawa Tokyo Inst. Tech.(Suzukakedai Campus) Unbiased Likelihood Backpropagation Learning
Masashi Sekino, Katsumi Nitta (Tokyo Inst. of Tech.) NC2007-1
The error backpropagation is one of the popular methods for training an artificial neural network.When the error backpro... [more] NC2007-1
pp.1-6
 Results 1 - 9 of 9  /   
Choose a download format for default settings. [NEW !!]
Text format pLaTeX format CSV format BibTeX format
Copyright and reproduction : All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan