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Gatsby Computational Neuroscience Unit
Alexandra House . 17 Queen Square .  LONDON . WC1N 3AR . U.K.
Tel +44 (0) 20 7679 1176 . Fax +44 (0) 20 7679 1173

Geoffrey E. Hinton's Publications

Backpropagation Learning

Online versions [if available] can be found in my chronological publications

  • Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986)
    Learning representations by back-propagating errors.
    Nature, 323, 533--536.
  • Hinton, G. E. (1986)
    Learning distributed representations of concepts.
    Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, Mass. Reprinted in Morris, R. G. M. editor, Parallel Distributed Processing: Implications for Psychology and Neurobiology, Oxford University Press, Oxford, UK
  • Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986)
    Learning internal representations by error propagation.
    In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations Volume 1: Foundations, MIT Press, Cambridge, MA.
  • Plaut, D., Nowlan, S. and Hinton, G. E. (1986) 
    Experiments on learning by back-propagation.
    Technical Report CMU-CS-86-126. Department of Computer Science, Carnegie-Mellon University.
  • Hinton, G. E. (1987)
    Learning translation invariant recognition in a massively parallel network.
    In Goos, G. and Hartmanis, J., editors, PARLE: Parallel Architectures and Languages Europe, pages 1--13, Lecture Notes in Computer Science, Springer-Verlag, Berlin.
  • Hinton, G. E. and Plaut, D. C. (1987)
    Using fast weights to deblur old memories.
    Proceedings of the Ninth Annual Conference of the Cognitive Science Society, Seattle, WA
  • Nowlan. S. J. and Hinton, G. E. (1992)
    Simplifying neural networks by soft weight sharing.
    Neural Computation, 4, 173-193.
  • Hinton, G. E. and van Camp, D. (1993)
    Keeping neural networks simple by minimizing the description length of the weights.
    Sixth ACM Conference on Computational Learning Theory, Santa Cruz, July 1993 .

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