Abstract
The graphics processing unit has evolved through the years into the powerful resource for general purpose computing. We present in this article the implementation of Extended Kalman filter used for recurrent neural networks training, which most computational intensive tasks are performed on the GPU. This approach achieves significant speedup of neural network training process for larger networks.
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Keywords
- Graphic Processing Unit
- Hide Neuron
- Extend Kalman Filter
- Recurrent Neural Network
- Cholesky Factorization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2008 Springer-Verlag Berlin Heidelberg
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Trebatický, P., Pospíchal, J. (2008). Neural Network Training with Extended Kalman Filter Using Graphics Processing Unit. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_21
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DOI: https://doi.org/10.1007/978-3-540-87559-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87558-1
Online ISBN: 978-3-540-87559-8
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