Abstract
Training recurrent networks is generally believed to be a difficult task. Excessive training times and lack of convergence to an acceptable solution are frequently reported. In this paper we seek to explain the reason for this from a numerical point of view and show how to avoid problems when training. In particular we investigate ill-conditioning, the need for and effect of regularization and illustrate the superiority of second-order methods for training
Original language | English |
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Title of host publication | IEEE Workshop on Neural Networks for Signal Processing VII |
Place of Publication | Piscataway, New Jersey |
Publisher | IEEE |
Publication date | 1997 |
Pages | 355-364 |
ISBN (Print) | 0-7803-4256-9 |
DOIs | |
Publication status | Published - 1997 |
Event | 1997 IEEE Workshop on Neural Networks for Signal Processing VII - Amelia Island, United States Duration: 24 Sept 1997 → 26 Sept 1997 Conference number: 7 https://ieeexplore.ieee.org/xpl/conhome/4900/proceeding |
Conference
Conference | 1997 IEEE Workshop on Neural Networks for Signal Processing VII |
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Number | 7 |
Country/Territory | United States |
City | Amelia Island |
Period | 24/09/1997 → 26/09/1997 |
Internet address |