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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SeMI, IPSJ-UBI, IPSJ-MBL | 2024-03-01 10:30 |
Fukuoka | A Preliminary Study on Parameter Optimization Using a Backpropagation Algorithm for a Neonatal Thermal Model Natsumi Sakamoto, Hiroki Kudo, Akira Uchiyama (Osaka Univ.), Keisuke Hamada (Nagasaki Harbor Medical Center), Eiji Hirakawa (Kagoshima City Hospital)  SeMI2023-81 |
Neonates need temperature management in incubators due to their underdeveloped thermoregulatory functions. Traditional m... [more] | SeMI2023-81 pp.60-65 |
|
| 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 |
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