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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP, NC (Joint) |
2020-01-24 17:05 |
Okinawa | Miyakojima Marine Terminal | [Invited Talk]
Neocognitron: Deep Convolutional Neural Network Kunihiko Fukushima (FLSI)  NLP2019-100 |
Recently, deep convolutional neural networks (deep CNN) have become very popular in the field of visual pattern recognit... [more] | NLP2019-100 pp.79-82 |
| MBE, NC (Joint) |
2018-03-13 11:15 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Application of U-Net to spine image extraction in CT image Mikoto Kamata, Masayuki Kikuchi (Tokyo Univ.of Tech.), Hayaru Shouno (Univ. of Electro-Communications.), Isao Hayashi (Kansai Univ.), Kunihiko Fukushima (Fuzzy Logic Systems Inst.) NC2017-81 |
In this study, we aimed at automatic extraction of spinal parts in CT images using deep learning as a foothold for autom... [more] | NC2017-81 pp.81-84 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS (Joint) [detail] |
2015-06-24 16:00 |
Okinawa | Okinawa Institute of Science and Technology | [Invited Talk]
Deep Convolutional Neural Network Neocognitron and its Advances Kunihiko Fukushima (FLSI) NC2015-3 IBISML2015-20 |
The neocognitron is a multi-layered convolutional network that can be trained to recognize visual patterns robustly. In ... [more] | NC2015-3 IBISML2015-20 pp.49-54(NC), pp.165-170(IBISML) |
| MBE, NC (Joint) |
2013-03-13 13:45 |
Tokyo | Tamagawa University | Three-staged Neocognitron: Optimal Thereshold and Thinning-out of Cells {Chihiro Yamamoto, Isao Hayashi (Kansai Univ.), Kunihiko Fukushima (FLSI) NC2012-143 |
The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. In the ... [more] |
NC2012-143 pp.55-60 |
| MBE, NC (Joint) |
2012-03-14 13:20 |
Tokyo | Tamagawa University | Training Multi-layered Neural Network Neocognitron Kunihiko Fukushima NC2011-128 |
This paper proposes new learning rules suited for training multi-layered neural networks and apply them to the neocognit... [more] | NC2011-128 pp.39-44 |
| NC, MBE (Joint) |
2010-03-11 16:00 |
Tokyo | Tamagawa University | Neocognitron Trained by a New Competitive Learning Kunihiko Fukushima, Isao Hayashi (Kansai Univ.), Hayaru Shouno (Univ. of Electro-Comm.), Masayuki Kikuchi, Yuki Makino (Tokyo Univ. of Tech.) NC2009-155 |
The "neocognitron" is a hierarchical multilayered neural network capable of robust visual pattern recognition. It acqui... [more] | NC2009-155 pp.397-402 |
| NC, MBE (Joint) |
2010-03-11 16:25 |
Tokyo | Tamagawa University | Edge Extraction for the Neocognitron Yuki Makino, Masayuki Kikuchi (Tokyo Univ. of Technology), Kunihiko Fukushima, Isao Hayashi (Kansai Univ.), Hayaru Shouno (Univ. of Electro-Communications) NC2009-156 |
Neural network model neocognitron has an ability of robust visual pattern recognition. Feature-extracting cells, called ... [more] | NC2009-156 pp.403-406 |
| NC, MBE (Joint) |
2008-03-14 10:50 |
Tokyo | Tamagawa Univ | Neural Network Capable of Amodal Completion Kunihiko Fukushima (Kansai Univ.) NC2007-189 |
When some parts of a pattern are occluded by other objects, the visual system can often estimate the shape of missing po... [more] | NC2007-189 pp.457-462 |
| NC | 2007-03-15 10:10 |
Tokyo | Tamagawa University | Interpolating Vectors for Robust Pattern Recognition Kunihiko Fukushima, Isao Hayashi (Kansai Univ.) |
This paper proposes a powerful algorithm for pattern recognition, which uses \textit{interpolating vectors} for classify... [more] | NC2006-171 pp.105-110 |
| NC | 2007-03-16 10:10 |
Tokyo | Tamagawa University | Neural Network model for local motion extraction Kazuya Tohyama (Tokyo Univ. of Tech.), Kunihiko Fukushima (Kansai Univ.) |
[more] | |
| NC | 2006-03-15 16:00 |
Tokyo | Tamagawa University | [Special Talk]
Visual Information Processing with Neural Networks Kunihiko Fukushima (Tokyo Univ. of Tech.) |
Modeling neural networks is a powerful approach to uncover the mechanism of the brain, and its results are ready to use ... [more] | NC2005-124 pp.109-114 |
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