Learning to Learn Using Gradient Descent-Reference-Cited by-同舟云学术

Learning to Learn Using Gradient Descent

Published:2001 Issue: Volume: Page:87-94
ISSN:0302-9743
Container-title:Artificial Neural Networks — ICANN 2001
language:
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Author:

Hochreiter Sepp,Younger A. Steven,Conwell Peter R.

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/3-540-44668-0_13

Reference19 articles.

1. R. Caruana. Learning many related tasks at the same time with backpropagation. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems 7, pages 657–664. The MIT Press, 1995.

2. D. Chalmers. The evolution of learning: An experiment in genetic connectionism. In D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, editors, Proc. of the 1990 Con. Models Summer School, pages 81–90. Morgan Kaufmann, 1990.

3. N. E. Cotter and P. R. Conwell. Fixed-weight networks can learn. In Int. Joint Conference on Neural Networks, volume II, pages 553–559. IEEE, NY, 1990.

4. H. Ellis. Transfer of Learning. MacMillan, New York, NY, 1965.

5. J. L. Elman. Finding structure in time. Technical Report CRL 8801, Center for Research in Language, University of California, San Diego, 1988.

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