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    NIPS
    1998
    132views Information Technology» more  NIPS 1998»
    Learning a Continuous Hidden Variable Model for Binary Data
    13 years 11 months ago
    Learning a Continuous Hidden Variable Model for Binary Data
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    A directed generative model for binary data using a small number of hidden continuous units is investigated. A clipping nonlinearity distinguishes the model from conventional prin...
    Daniel D. Lee, Haim Sompolinsky
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    NIPS
    1998
    89views Information Technology» more  NIPS 1998»
    Orientation, Scale, and Discontinuity as Emergent Properties of Illusory Contour Shape
    13 years 11 months ago
    Orientation, Scale, and Discontinuity as Emergent Properties of Illusory Contour Shape
    Download  www.iro.umontreal.ca
    A recent neural model of illusory contour formation is based on a distribution of natural shapes traced by particles moving with constant speed in directions given by Brownian mot...
    Karvel K. Thornber, Lance R. Williams
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    NIPS
    2001
    112views Information Technology» more  NIPS 2001»
    A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing
    13 years 11 months ago
    A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing
    Download  www.stanford.edu
    Narayanan and Jurafsky (1998) proposed that human language comprehension can be modeled by treating human comprehenders as Bayesian reasoners, and modeling the comprehension proce...
    S. Narayanan, Daniel Jurafsky
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    NIPS
    1998
    80views Information Technology» more  NIPS 1998»
    Efficient Bayesian Parameter Estimation in Large Discrete Domains
    13 years 11 months ago
    Efficient Bayesian Parameter Estimation in Large Discrete Domains
    Download  www.cs.huji.ac.il
    In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discreteoutcomes,most of which do not appearin the training ...
    Nir Friedman, Yoram Singer
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    NIPS
    1998
    112views Information Technology» more  NIPS 1998»
    Computational Differences between Asymmetrical and Symmetrical Networks
    13 years 11 months ago
    Computational Differences between Asymmetrical and Symmetrical Networks
    Download  www.gatsby.ucl.ac.uk
    Symmetrically connected recurrent networks have recently been used as models of a host of neural computations. However, biological neural networks have asymmetrical connections, at...
    Zhaoping Li, Peter Dayan
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