Zobrazeno 1 - 10
of 144
pro vyhledávání: '"Kunihiko Fukushima"'
Publikováno v:
IEEE Access, Vol 12, Pp 51818-51827 (2024)
Circuit design requires trial and error in both prototyping and simulation owing to the high degrees of freedom and mutual interference between the components. In this study, we propose a novel approach to address this challenge by introducing a tran
Externí odkaz:
https://doaj.org/article/ea0c491fad0548cc8c8fd3fcbd05e2f9
Autor:
Kunihiko Fukushima
Publikováno v:
IEEE Transactions on Systems, Man, and Cybernetics: Systems. 51:76-90
Deep convolutional neural networks (deep CNNs) show a large power for robust recognition of visual patterns. The neocognitron, which was first proposed by Fukushima (1979), is recognized as the origin of deep CNNs. Its architecture was suggested by t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2aa723b9ca5a9aa5781c79f2353ee5e
https://doi.org/10.1109/tsmc.2020.3042785
https://doi.org/10.1109/tsmc.2020.3042785
Autor:
Kunihiko Fukushima
Publikováno v:
Nonlinear Theory and Its Applications, IEICE. 10:304-321
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba0d1ddd9a1e67629b29ba183a41d487
https://doi.org/10.1587/nolta.10.304
https://doi.org/10.1587/nolta.10.304
Autor:
Kunihiko Fukushima
Publikováno v:
Neural networks : the official journal of the International Neural Network Society. 119
In many deep neural networks for pattern recognition, the input pattern is classified in the deepest layer based on features extracted through intermediate layers. IntVec (interpolating-vector) is known to be a powerful method for this process of cla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b62f0fe58d272646f7d2bd55dbf25d4c
https://pubmed.ncbi.nlm.nih.gov/31499356
https://pubmed.ncbi.nlm.nih.gov/31499356
Autor:
Kunihiko Fukushima
Publikováno v:
IJCNN
The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbbe0a089a4ae8811ae84e3f1e0b6e8b
https://pubmed.ncbi.nlm.nih.gov/29126068
https://pubmed.ncbi.nlm.nih.gov/29126068
Publikováno v:
ICONIP (2)
The neocognitron is a hierarchical, multi-layered neural network capable of robust visual pattern recognition. The neocognitron acquires the ability to recognize visual patterns through learning. The winner-kill-loser is a competitive learning rule r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af5df44cfbec514e15ce8de6d5d01c16
https://doi.org/10.1016/j.neucom.2012.05.038
https://doi.org/10.1016/j.neucom.2012.05.038
Autor:
Zhao Xiongxin, Toshisada Mariyama, Petros T. Boufounos, Manabu Hagiwara, Matsumoto Wataru, Kunihiko Fukushima
Publikováno v:
Neural Information Processing ISBN: 9783319466804
ICONIP (4)
ICONIP (4)
We present a new deep neural network architecture, motivated by sparse random matrix theory that uses a low-complexity embedding through a sparse matrix instead of a conventional stacked autoencoder. We regard autoencoders as an information-preservin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f77aae307021753a5965549d94534fd7
https://doi.org/10.1007/978-3-319-46681-1_48
https://doi.org/10.1007/978-3-319-46681-1_48
Publikováno v:
Neural Information Processing ISBN: 9783319466712
ICONIP (2)
ICONIP (2)
Structures of neural networks are usually designed by experts to fit target problems. This study proposes a method to automate small network design for a regression problem based on the Add-if-Silent AiS function used in the neocognitron. Because the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::73140e5a449d8c1333af66918daeae1e
https://doi.org/10.1007/978-3-319-46672-9_32
https://doi.org/10.1007/978-3-319-46672-9_32
Autor:
Kunihiko Fukushima
Publikováno v:
Neural Networks. 24:767-778
The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It has been demonstrated that recent versions of the neocognitron exhibit excellent performance for recognizing handwritten digits. When cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ac73abf53bb22d11f39f2bc90d2ec71
https://doi.org/10.1016/j.neunet.2011.03.017
https://doi.org/10.1016/j.neunet.2011.03.017
Autor:
Kunihiko Fukushima
Publikováno v:
Neural Networks. 23:528-540
When some parts of a pattern are occluded by other objects, the visual system can often estimate the shape of occluded contours from visible parts of the contours. This paper proposes a neural network model capable of such function, which is called a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d960094e9e8dd98aeec879ce347f9d75
https://doi.org/10.1016/j.neunet.2009.10.002
https://doi.org/10.1016/j.neunet.2009.10.002