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    NIPS
    1998
    156views Information Technology» more  NIPS 1998»
    Learning Nonlinear Dynamical Systems Using an EM Algorithm
    13 years 8 months ago
    Learning Nonlinear Dynamical Systems Using an EM Algorithm
    Download  www.cs.nyu.edu
    The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
    Zoubin Ghahramani, Sam T. Roweis
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    NIPS
    1997
    96views Information Technology» more  NIPS 1997»
    Learning Generative Models with the Up-Propagation Algorithm
    13 years 8 months ago
    Learning Generative Models with the Up-Propagation Algorithm
    Download  hebb.mit.edu
    Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
    Jong-Hoon Oh, H. Sebastian Seung
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    NIPS
    1996
    128views Information Technology» more  NIPS 1996»
    Neural Models for Part-Whole Hierarchies
    13 years 8 months ago
    Neural Models for Part-Whole Hierarchies
    Download  riesenhuberlab.neuro.georgetown.edu
    We present a connectionist method for representing images that explicitlyaddresses their hierarchicalnature. It blends data fromneuroscience about whole-object viewpoint sensitive...
    Maximilian Riesenhuber, Peter Dayan
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    NIPS
    1996
    106views Information Technology» more  NIPS 1996»
    Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems
    13 years 8 months ago
    Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems
    Download  www.cis.upenn.edu
    In cellular telephone systems, an important problem is to dynamically allocate the communication resource channels so as to maximize service in a stochastic caller environment. Th...
    Satinder P. Singh, Dimitri P. Bertsekas
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    BMCBI
    2007
    91views more  BMCBI 2007»
    A machine learning approach for the identification of odorant binding proteins from sequence-derived properties
    13 years 7 months ago
    A machine learning approach for the identification of odorant binding proteins from sequence-derived properties
    Download  www.biomedcentral.com
    Background: Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as od...
    Ganesan Pugalenthi, E. Ke Tang, Ponnuthurai N. Sug...
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