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Limitations and Future Trends in Neural Computation

  1. 1.
    Šíma, Jiří
    Energy-Based Computation with Symmetric Hopfield Nets.
    Limitations and Future Trends in Neural Computation. Amsterdam: IOS Press, 2003 - (Ablameyko, S.; Gori, M.; Goras, L.; Piuri, V.), s. 45-70. NATO Science Series, 186. ISBN 1-58603-324-7
    http://hdl.handle.net/11104/0124434

    Cited: 4

    --- SERPEN, G. Hopfield network as static optimizer: Learning the weights and eliminating the guesswork. NEURAL PROCESSING LETTERS. ISSN 1370-4621, FEB 2008, vol. 27, no. 1, p. 1-15. [WOS]
    --- Serpen G. Adaptation in Weight Space through Gradient Descent for Hopfield Network as Static Optimizer: Is it Feasible?” to appear in proceedings for the Artificial Neural Networks in Engineering Conference to be held in St. Louis, MO, November 2007.
    --- Gardasevic, V. - Muller, R. - Ryan, D. - Lundheim, L. - Oien, G. Lattice-Reduction Aided HNN for Vector Precoding. Proceedings of ISITA 2010. IEEE, 2010, p. 37-41.
    --- Gardasevic, V. - Muller, R. - Oien, C. Hopfield Neural Networks for Vector Precoding. Proceedings of Int. Zurich Seminar on Communications (IZS), March 3-5, 2010, p. 66-69.

Number of the records: 1  

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