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    NIPS
    1996
    136views Information Technology» more  NIPS 1996»
    Adaptively Growing Hierarchical Mixtures of Experts
    14 years 11 months ago
    Adaptively Growing Hierarchical Mixtures of Experts
    Download  www.ri.cmu.edu
    We propose a novelapproach to automaticallygrowing and pruning Hierarchical Mixtures of Experts. The constructive algorithm proposed here enables large hierarchies consisting of s...
    Jürgen Fritsch, Michael Finke, Alex Waibel
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    NIPS
    1996
    134views Information Technology» more  NIPS 1996»
    Why did TD-Gammon Work?
    14 years 11 months ago
    Why did TD-Gammon Work?
    Download  www.cse.unsw.edu.au
    Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal difference learning for other applications or ...
    Jordan B. Pollack, Alan D. Blair
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    NIPS
    1996
    84views Information Technology» more  NIPS 1996»
    Solving the Ill-Conditioning in Neural Network Learning
    14 years 11 months ago
    Solving the Ill-Conditioning in Neural Network Learning
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    Abstract. In this paper we investigate the feed-forward learning problem. The well-known ill-conditioning which is present in most feed-forward learning problems is shown to be the...
    P. Patrick van der Smagt, Gerd Hirzinger
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    NIPS
    1996
    92views Information Technology» more  NIPS 1996»
    Reconstructing Stimulus Velocity from Neuronal Responses in Area MT
    14 years 11 months ago
    Reconstructing Stimulus Velocity from Neuronal Responses in Area MT
    Download  www.physiol.ox.ac.uk
    We employed a white-noise velocity signal to study the dynamics of the response of single neurons in the cortical area MT to visual motion. Responses were quantified using reverse...
    Wyeth Bair, James R. Cavanaugh, J. Anthony Movshon
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    NIPS
    1996
    124views Information Technology» more  NIPS 1996»
    Are Hopfield Networks Faster than Conventional Computers?
    14 years 11 months ago
    Are Hopfield Networks Faster than Conventional Computers?
    Download  www.eng.unt.edu
    It is shown that conventional computers can be exponentially faster than planar Hopfield networks: although there are planar Hopfield networks that take exponential time to conver...
    Ian Parberry, Hung-Li Tseng
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