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NIPS
1993

Credit Assignment through Time: Alternatives to Backpropagation

8 years 10 months ago
Credit Assignment through Time: Alternatives to Backpropagation
Learning to recognize or predict sequences using long-term context has many applications. However, practical and theoretical problems are found in training recurrent neural networks to performtasks in which input/output dependencies span longintervals. Starting from a mathematical analysis of the problem, we consider and compare alternative algorithms and architectures on tasks for which the span of the input/output dependencies can be controlled. Results on the new algorithms show performance qualitatively superior to that obtained with backpropagation.
Yoshua Bengio, Paolo Frasconi
Added 02 Nov 2010
Updated 02 Nov 2010
Type Conference
Year 1993
Where NIPS
Authors Yoshua Bengio, Paolo Frasconi
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