Hiromitsu Awano Shun Nishide Hiroaki Arie Jun Tani Toru Takahashi Hiroshi G. Okuno Tetsuya Ogata
NEURAL INFORMATION PROCESSING, PT III 7064 323-+ 2011年 [査読有り]
The objective of our study is to find out how a sparse structure affects the performance of a recurrent neural network (RNN). Only a few existing studies have dealt with the sparse structure of RNN with learning like Back Propagation Through Time ...
Yang Zhang Tetsuya Ogata Shun Nishide Toru Takahashi Hiroshi G. Okuno
in Proc. of Joint 5th Int. Conf. on Soft Computing and Intelligent Systems and 11th International Symposium on advanced Intelligent Systems (SCIS & ISIS 2010) 378-383 2010年12月 [査読有り]
13th Asia Pacific Signal and Information Processing Association Annual Summit and Conference 2021年12月 Asia Pacific Signal and Information Processing Association
13th Asia Pacific Signal and Information Processing Association Annual Summit and Conference 2021年12月 Asia Pacific Signal and Information Processing Association
国際学部
デザイン工学部