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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP, MSS (Joint) |
2019-03-14 15:10 |
Fukui | Bunkyo Camp., Univ. of Fukui | Automatic classification of human behavior using wearable devices Keita Sato, Masafumi Chida, Yoshihiro Hayakawa, Nahomi Fujiki (NITS)  NLP2018-129 |
(To be available after the conference date) [more] | NLP2018-129 pp.27-30 |
| NLP | 2016-12-12 16:25 |
Aichi | Chukyo Univ. | The Relationship between Modular Neural Network and Noise Effect Kou Muraoka, Rui Yoshida, Yoshihiro Hayakawa (NIT, Sendai) NLP2016-91 |
Some of combinatorial optimization problems have problems that the number of solutions to be searched increases with the... [more] | NLP2016-91 pp.39-44 |
| NLP | 2016-03-25 11:30 |
Kyoto | Kyoto Sangyo Univ. | Image Feature Extraction based on Neural Network and Its Application Yoshihiro Hayakawa, Takanori Oonuma, Hideyuki Kobayashi, Akiko Takahashi, Shinji Chiba, Nahomi Fujiki (NIT, Sendai) NLP2015-153 |
[more] | NLP2015-153 pp.63-67 |
| NLP, CCS | 2015-06-12 10:15 |
Tokyo | Waseda Univerisity | Dynamics analysis of Modular Neural Network using Toy Model Yoshihiro Hayakawa, Masayuki Matumori (NIT,Sendai) NLP2015-57 CCS2015-19 |
Combinatorial optimization problems cause exponential increases of calculation time in terms of a problem size. In case ... [more] | NLP2015-57 CCS2015-19 pp.109-113 |
| NLP | 2015-01-26 17:10 |
Oita | Compal Hall | The dependence to the active neuron's parameter of the searching performance for TSP's solutions by using DS-net Hikaru Okuda, Yoshihiro Hayakawa (NIT, Sendai) NLP2014-127 |
The Inverse function Delayed (ID) model has been proposed as one of the active neuron model. The ID model is expected as... [more] | NLP2014-127 pp.83-88 |
| MBE, NC (Joint) |
2014-11-21 11:00 |
Miyagi | Tohoku University | A Comparison of Back Propagation Learning between the Inverse-function Delayless Model and a Conventional Model Yuta Horiuchi (Tohoku Univ), Yoshihiro Hayakawa (SNCT), Takeshi Onomi, Koji Nakajima (Tohoku Univ) NC2014-26 |
For the combinatorial optimization problem using the hopfield model, avoidance of the local minimum problem is important... [more] | NC2014-26 pp.7-10 |
| MBE, NC (Joint) |
2014-11-21 11:25 |
Miyagi | Tohoku University | The Relation between Dispersion of Initial Values and Pre-training of Deep Neural Networks Seitaro Shinagawa (Tohoku univ.), Yoshihiro Hayakawa (SNCT), Takeshi Onomi, Koji Nakajima (Tohoku univ.) NC2014-27 |
[more] | NC2014-27 pp.11-14 |
| NLP | 2014-06-30 16:00 |
Miyagi | Tohoku Univ. | Backpropagation learning using inverse function delay-less model Yuta Horiuchi (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Takeshi Onomi, Koji Nakajima (Tohoku Univ.) NLP2014-25 |
The Inverse function Delayed (ID) model has been proposed as one of novel neural models. ID model has a oscillation capa... [more] | NLP2014-25 pp.27-30 |
| NLP | 2014-06-30 16:25 |
Miyagi | Tohoku Univ. | Study on the hardware of the Bidirectional Associative Memories by using the Inverse Function Delayless model Chunyu Bao, Takeshi Onomi, Yoshihiro Hayakawa, Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2014-26 |
In conventional macro models such as the Hopfield model, the problems that are caused by the solution of the network not... [more] | NLP2014-26 pp.31-36 |
| NLP | 2014-07-01 10:00 |
Miyagi | Tohoku Univ. | Learning Restricted Boltzmann Machine with discrete learning parameter Seitaro Shinagawa (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Takeshi Onomi, Koji Nakajima (Tohoku Univ.) NLP2014-27 |
Recently, the method of Deep Neural Network (DNN) with hierarchical learning has been remarkable for performance to solv... [more] | NLP2014-27 pp.37-40 |
| CPM, ED, SDM | 2014-05-28 14:00 |
Aichi | Optimization of the preparation condition of LiMn2O4 film Yoshihiro Hayakawa, Masaaki Isai, Yasumasa Tomita (Shizuoka Univ.) ED2014-23 CPM2014-6 SDM2014-21 |
[more] | ED2014-23 CPM2014-6 SDM2014-21 pp.27-32 |
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| NLP | 2014-01-21 13:30 |
Hokkaido | Niseko Park Hotel | DTN routing method by using neural networkas. Daisuke Sasaki (Tohoku Univ), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Koji Nakajima (Tohoku Univ) NLP2013-136 |
A Disruption tolerant Network (DTN) is studied as a communicating technique for the time when a network infrastructure w... [more] | NLP2013-136 pp.41-44 |
| NLP | 2014-01-21 13:50 |
Hokkaido | Niseko Park Hotel | Solving Optimization Problems Using DS-net and IDL model Yuto Watanabe (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2013-137 |
The Inverse function DelayLess (IDL) model has been proposed as one of novel neural models. Since the IDL model can set ... [more] | NLP2013-137 pp.45-50 |
| NLP | 2014-01-21 15:40 |
Hokkaido | Niseko Park Hotel | Neural Network learning using Inverse Function Delayless Model Yuta Horiuchi (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2013-142 |
The Inverse function Delayed (ID) model has been proposed as one of novel neural models. The ID model has an ability of ... [more] | NLP2013-142 pp.73-76 |
| NLP | 2014-01-22 10:20 |
Hokkaido | Niseko Park Hotel | Studies on the hardware of neural associative memory with a broad basin Jiang Jing (Tohoku Univ), Yoshihiro Hayakawa (Sendai NT), Shigeo Sato, Koji Nakajima (Tohoku Univ) NLP2013-147 |
Abstract Because associative operation of the Hopfield neural network model are trapped in spurious memory, the associat... [more] | NLP2013-147 pp.99-102 |
| NLP | 2013-10-29 13:30 |
Kagawa | Sanport Hall Takamatsu | Discussion about the effectiveness of a DS-net and an active neuron model Yoshihiro Hayakawa, Hikaru Okuda (Sendai NCT.), Yuto Watanabe, Koji Nakajima (Tohoku Univ.) NLP2013-100 |
The active neuron model, which means an active region in output space, is an effective tool to avoid local minimum pro... [more] |
NLP2013-100 pp.159-164 |
| NLP | 2013-10-29 13:45 |
Kagawa | Sanport Hall Takamatsu | Fast Calculation of High functional Neural Network by using GPU Kouta Tanno (Tohoku Univ.), Yoshihiro Hayakawa (Sendai NCT) NLP2013-101 |
A GPU can compute in highly parallel because it has many cores (processing units). Though it is reported that a high fun... [more] | NLP2013-101 pp.165-168 |
| NLP | 2012-12-18 09:45 |
Fukui | Fukui City Communication Plaza | Inverse Function Delayless (IDL) Model Yuto Watanabe (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2012-98 |
The Inverse function Delayed (ID) model has been proposed as one of novel neural models. The ID model has the negative r... [more] | NLP2012-98 pp.57-60 |
| NLP | 2012-12-18 10:10 |
Fukui | Fukui City Communication Plaza | Hardware implementation of the discrete Inverse-function Delayed network with Higher order synaptic Connections Kosuke Matsui (Tohoku Univ.), Yoshihiro Hayakawa (Sendai N.C.T.), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2012-99 |
The HC-ID network has been proposed as a network model that avoids local minima when it is applied to combinatorial opti... [more] | NLP2012-99 pp.61-64 |
| NLP | 2012-12-18 10:35 |
Fukui | Fukui City Communication Plaza | Optimization of Scheduling in Disruption-Tolerant Networks by Neural Network Daisuke Sasaki (Tohoku Univ), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Koji Nakajima (Tohoku Univ) NLP2012-100 |
A Disruption tolerant Network (DTN) is studied as a communicating technique when a network infrastructure was destroyed ... [more] | NLP2012-100 pp.65-68 |
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