Kunihiko Fukushima (Japanese: 福島 邦彦, born 16 March 1936) is a Japanese computer scientist, most noted for his work on artificial neural networks and deep learning. He is currently working part-time as a senior research scientist at the Fuzzy Logic Systems Institute in Fukuoka, Japan.[1]
Kunihiko Fukushima | |
|---|---|
| Born | 16 March 1936 |
| Citizenship | Japan |
| Alma mater | Kyoto University |
| Known for | Artificial neural networks, Neocognitron, Convolutional neural network architecture, Unsupervised learning, Deep learning, ReLU activation function |
| Awards | IEICE Achievement Award and Excellent Paper Awards IEEE Neural Networks Pioneer Award APNNA Outstanding Achievement Award JNNS Excellent Paper Award INNS Helmholtz Award Bower Award and Prize for Achievement in Science |
| Scientific career | |
| Fields | Computer science |
| Institutions | Fuzzy Logic Systems Institute |
Notable scientific achievements
editIn 1980, Fukushima published the neocognitron,[2][3] the original deep convolutional neural network (CNN) architecture.[4][5] Fukushima proposed several supervised and unsupervised learning algorithms to train the parameters of a deep neocognitron such that it could learn internal representations of incoming data.[3][6] Today, however, the CNN architecture is usually trained through backpropagation. This approach is now heavily used in computer vision.[5][7]
In 1969 Fukushima introduced the ReLU (Rectifier Linear Unit) activation function in the context of visual feature extraction in hierarchical neural networks, which he called "analog threshold element".[8][9] (Though the ReLU was first used by Alston Householder in 1941 as a mathematical abstraction of biological neural networks.[10]) As of 2017[update] it is the most popular activation function for deep neural networks.[11]
Education and career
editIn 1958, Fukushima received his Bachelor of Engineering in electronics from Kyoto University.[1] He became a senior research scientist at the NHK Science & Technology Research Laboratories. In 1989, he joined the faculty of Osaka University.[1] In 1999, he joined the faculty of the University of Electro-Communications. In 2001, he joined the faculty of Tokyo University of Technology. From 2006 to 2010, he was a visiting professor at Kansai University.[1]
Fukushima acted as founding president of the Japanese Neural Network Society (JNNS). He also was a founding member on the board of governors of the International Neural Network Society (INNS), and president of the Asia-Pacific Neural Network Assembly (APNNA).[1] He was one of the board of governors of the International Neural Network Society (INNS) in 1989-1990 and 1993-2005.[12]
Awards
editIn 2020 Fukushima received the Bower Award and Prize for Achievement in Science.[13] He also received the IEICE Achievement Award and Excellent Paper Awards, the IEEE Neural Networks Pioneer Award, the APNNA Outstanding Achievement Award, the JNNS Excellent Paper Award and the INNS Helmholtz Award.[1]
External links
editReferences
edit- ^ a b c d e f CIS Oral History Project (Don Wunsch) (2015). "Interview with Kunihiko Fukushima". IEEE TV. Retrieved 2019-02-27.
- ^ Fukushima, Neocognitron (1980). "A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position". Biological Cybernetics. 36 (4): 193–202. doi:10.1007/bf00344251. PMID 7370364. S2CID 206775608.
- ^ a b Fukushima, K. (2007). "Neocognitron". Scholarpedia. 2 (1): 1717. Bibcode:2007SchpJ...2.1717F. doi:10.4249/scholarpedia.1717.
- ^ Fogg, Andrew (2017). "A History of Deep Learning". import.io. Retrieved 2019-02-27.
- ^ a b Schmidhuber, Jürgen (2015). "Deep Learning". Scholarpedia. 10 (11): 1527–54. CiteSeerX 10.1.1.76.1541. doi:10.1162/neco.2006.18.7.1527. PMID 16764513. S2CID 2309950.
- ^ Fukushima, Kunihiko (2018). "Video: Artificial Vision by Deep CNN Neocognitron". Youtube. Retrieved 2019-03-25.
- ^ LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015). "Deep learning" (PDF). Nature. 521 (7553): 436–444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442. S2CID 3074096.
- ^ Fukushima, K. (1969). "Visual feature extraction by a multilayered network of analog threshold elements". IEEE Transactions on Systems Science and Cybernetics. 5 (4): 322–333. doi:10.1109/TSSC.1969.300225.
- ^ Fukushima, K.; Miyake, S. (1982). "Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition". Competition and Cooperation in Neural Nets. In Competition and Cooperation in Neural Nets, Lecture Notes in Biomathematics. Vol. 45. Springer. pp. 267–285. doi:10.1007/978-3-642-46466-9_18. ISBN 978-3-540-11574-8.
- ^ Householder, Alston S. (June 1941). "A theory of steady-state activity in nerve-fiber networks: I. Definitions and preliminary lemmas". The Bulletin of Mathematical Biophysics. 3 (2): 63–69. doi:10.1007/BF02478220. ISSN 0007-4985.
- ^ Ramachandran, Prajit; Barret, Zoph; Quoc, V. Le (October 16, 2017). "Searching for Activation Functions". arXiv:1710.05941 [cs.NE].
- ^ INNS Board of Governors archive
- ^ "Kunihiko Fukushima". The Franklin Institute. 2020-01-25. Retrieved 2020-01-27.