r9 - 07 Oct 2009 - 14:00:48 - MichaelMandelYou are here: TWiki >  Public Web  > DeepNetworks > ReadingOnDeepNetworks > RecurrentNeuralNetBibliography

Bibliography of Recurrent Neural Network papers

(in chronological order)

  • D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representation by error propagation," in Parallel Distributed Processing, D. E. Rumelhart and J. L. McClelland, Eds. Cambridge MA: MIT Press, Bradford Books, vol. 1, 1986, pp. 318-362. pdf
  • J. L. Elman, "Finding structure in time," Cognitive Science, vol. 14, pp. 179-211, 1990. pdf
  • M. C. Mozer, "Induction of multiscale temporal structure," in Advances in Neural Information Processing Systems 4, Moody, Hanson, and Lippman, Eds. San Matteo, CA: Mogan Kaufmann, 1992, pp. 275-282. pdf
  • J. Schmidhuber, "Learning complex, extended sequences using the principle of history compression," Neural Computation, vol. 4, no. 2, pp. 234-242, March 1992. www
  • Y. Bengio, P. Simard, and P. Frasconi, "Learning long-term dependencies with gradient descent is difficult," IEEE Transactions on Neural Networks, vol. 5, no. 2, pp. 157-166, March 1994. www
  • Y. Bengio and P. Frasconi, "Diffusion of context and credit information in markovian models," Journal of Artificial Intelligence Research, vol. 3, pp. 223-244, 1995. pdf
  • T. Lin, B. G. Horne, P. Tino, and C. L. Giles, "Learning long-term dependencies is not as difficult with NARX recurrent neural networks," Institute for Advanced Computer Studies, University of Maryland, Tech. Rep. UMICAS-TR-95-78, 1995. www
  • B. A. Pearlmutter, "Gradient calculations for dynamic recurrent neural networks: a survey," IEEE Transactions on Neural Networks, vol. 6, no. 5, pp. 1212-1228, September 1995. www
  • S. ElHihi and Y. Bengio, "Hierarchical recurrent neural networks for long-term dependencies," in Advances in Neural Information Processing Systems 8 (NIPS'95), D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, Eds. MIT Press, Cambridge, MA, 1996, pp. 493-499. pdf
  • S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, November 1997. www
  • S. Hochreiter, F. F. Informatik, Y. Bengio, P. Frasconi, and J. Schmidhuber, "Gradient flow in recurrent nets: the difficulty of learning long-term dependencies," in Field Guide to Dynamical Recurrent Networks, J. Kolen and S. Kremer, Eds. IEEE Press, 2000. www

-- MichaelMandel - 30 Sep 2009

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