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Abstract: Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases. These... (Update)
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BibTeX entry: (Update)
Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5 (2), 157--166. http://citeseer.ist.psu.edu/bengio94learning.html More
@article{ bengio94learning,
author = "Yoshua Bengio and Patrice Simard and Paolo Frasconi",
title = "Learning Long-Term Dependencies with Gradient Descent is Difficult",
journal = "IEEE Transactions on Neural Networks",
volume = "5",
number = "2",
month = "March",
pages = "157--166",
year = "1994",
url = "citeseer.ist.psu.edu/bengio94learning.html" }
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