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All Technical Committee Conferences  (Searched in: Recent 10 Years)

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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
EST, MW, EMCJ, IEE-EMC [detail] 2024-10-17
09:00		 Akita Akita Art Village Onsen Yupopo
(Primary: On-site, Secondary: Online)		 Integrating Circuits and Graph Neural Networks for Circuit Constants Optimization
Yusuke Yamakaji (Mitsubishi Electric/Univ. of Electro-Communications), Hayaru Shouno (The University of Electro-Communications), Kunihiko Fukushima (Fuzzy Logic Systems Inst.) EMCJ2024-36 MW2024-89 EST2024-60		 Circuit design requires empirical rules based on trial and error through prototyping and simulation because each compone... [more] EMCJ2024-36 MW2024-89 EST2024-60
pp.1-6
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)
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