Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Shinozaki, Takashi"'
Autor:
Shinozaki, Takashi
This study proposes a novel biologically-motivated learning method for deep convolutional neural networks (CNNs). The combination of CNNs and back propagation (BP) learning is the most powerful method in recent machine learning regimes. However, it r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::322a03b412fe393460c3c229196dd3f4
http://arxiv.org/abs/2001.01121
http://arxiv.org/abs/2001.01121
Autor:
Shinozaki, Takashi
In this study, we propose the integration of competitive learning into convolutional neural networks (CNNs) to improve the representation learning and efficiency of fine-tuning. Conventional CNNs use back propagation learning, and it enables powerful
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5591fceb189a09f5398ae5275489d0ea
Autor:
Shinozaki, Takashi
In this study, we propose a novel deep neural network and its supervised learning method that uses a feedforward supervisory signal. The method is inspired by the human visual system and performs human-like association-based learning without any back
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75752dfc9fc53675165e684a960571e0
http://arxiv.org/abs/1710.09574
http://arxiv.org/abs/1710.09574
Autor:
Shinozaki, Takashi, Naruse, Yasushi
We propose a novel learning method for multilayered neural networks which uses feedforward supervisory signal and associates classification of a new input with that of pre-trained input. The proposed method effectively uses rich input information in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::018cd6ebff246b17846c3bb6dc318f44
http://arxiv.org/abs/1312.5845
http://arxiv.org/abs/1312.5845