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Volumn 2130, Issue , 2001, Pages 87-94

Learning to learn using gradient descent

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; LEARNING SYSTEMS; NEURAL NETWORKS; RECURRENT NEURAL NETWORKS;

EID: 84958985283     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44668-0_13     Document Type: Conference Paper
Times cited : (604)

References (19)
  • 1
    • 85153936556 scopus 로고
    • Learning many related tasks at the same time with backpropagation
    • G. Tesauro, D. Touretzky, and T. Leen, editors, The MIT Press
    • 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.
    • (1995) Advances in Neural Information Processing Systems , vol.7 , pp. 657-664
    • Caruana, R.1
  • 2
    • 0001793307 scopus 로고
    • The evolution of learning: An experiment in genetic connectionism
    • D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, editors, Morgan Kaufmann
    • 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.
    • (1990) Proc. Of the 1990 Con. Models Summer School , pp. 81-90
    • Chalmers, D.1
  • 5
    • 0004262806 scopus 로고
    • Technical Report CRL 8801, Center for Researchin Language, University of California, San Diego
    • J. L. Elman. Finding structure in time. Technical Report CRL 8801, Center for Researchin Language, University of California, San Diego, 1988.
    • (1988) Finding Structure in Time
    • Elman, J.L.1
  • 6
    • 0024082469 scopus 로고
    • Quantifying inductive bias: AI learning algorithms and Valiant’s learning framework
    • D. Haussler. Quantifying inductive bias: AI learning algorithms and Valiant’s learning framework. Artificial Intelligence, 36:177-221, 1988.
    • (1988) Artificial Intelligence , vol.36 , pp. 177-221
    • Haussler, D.1
  • 12
    • 0031186687 scopus 로고    scopus 로고
    • Shifting inductive bias with successstory algorithm, adaptive levin search, and incremental self-improvement
    • J. Schmidhuber, J. Zhao, and M. Wiering. Shifting inductive bias with successstory algorithm, adaptive levin search, and incremental self-improvement. Machine Learning, 28:105-130, 1997.
    • (1997) Machine Learning , vol.28 , pp. 105-130
    • Schmidhuber, J.1    Zhao, J.2    Wiering, M.3
  • 15
    • 0001164493 scopus 로고
    • Shift of bias for inductive concept learning
    • R. Michalski, J. Carbonell, and T. Mitchell, editors, Morgan Kaufmann
    • P. Utgoff. Shift of bias for inductive concept learning. In R. Michalski, J. Carbonell, and T. Mitchell, editors, Machine Learning, volume 2. Morgan Kaufmann, 1986.
    • (1986) Machine Learning , vol.2
    • Utgoff, P.1
  • 16
    • 0000903748 scopus 로고
    • Generalization of backpropagation withapplication to a recurrent gas market model
    • P. J. Werbos. Generalization of backpropagation withapplication to a recurrent gas market model. Neural Networks, 1, 1988.
    • (1988) Neural Networks , vol.1
    • Werbos, P.J.1
  • 18
    • 0001765578 scopus 로고
    • Gradient-based learning algorithms for recurrent networks and their computational complexity
    • Y. Chauvin and D. E. Rumelhart, editors
    • R. J. Williams and D. Zipser. Gradient-based learning algorithms for recurrent networks and their computational complexity. In Y. Chauvin and D. E. Rumelhart, editors, Back-propagation: Theory, Architectures and Applications. Hillsdale, 1992.
    • (1992) Back-Propagation: Theory, Architectures and Applications. Hillsdale
    • Williams, R.J.1    Zipser, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.