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    JMLR
    2012
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    Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks
    11 years 9 months ago
    Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks
    Download  www.cs.cmu.edu
    The expectation maximization (EM) algorithm is a popular algorithm for parameter estimation in models with hidden variables. However, the algorithm has several non-trivial limitat...
    Avneesh Singh Saluja, Priya Krishnan Sundararajan,...
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    JMLR
    2012
    159views Programming Languages» more  JMLR 2012»
    Randomized Optimum Models for Structured Prediction
    11 years 9 months ago
    Randomized Optimum Models for Structured Prediction
    Download  hips.seas.harvard.edu
    One approach to modeling structured discrete data is to describe the probability of states via an energy function and Gibbs distribution. A recurring difficulty in these models is...
    Daniel Tarlow, Ryan Prescott Adams, Richard S. Zem...
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    JMLR
    2012
    196views Programming Languages» more  JMLR 2012»
    A metric learning perspective of SVM: on the relation of LMNN and SVM
    11 years 9 months ago
    A metric learning perspective of SVM: on the relation of LMNN and SVM
    Download  jmlr.csail.mit.edu
    Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper...
    Huyen Do, Alexandros Kalousis, Jun Wang, Adam Wozn...
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    JMLR
    2012
    181views Programming Languages» more  JMLR 2012»
    On Nonparametric Guidance for Learning Autoencoder Representations
    11 years 9 months ago
    On Nonparametric Guidance for Learning Autoencoder Representations
    Download  www.dmi.usherb.ca
    Unsupervised discovery of latent representations, in addition to being useful for density modeling, visualisation and exploratory data analysis, is also increasingly important for...
    Jasper Snoek, Ryan Prescott Adams, Hugo Larochelle
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    JMLR
    2012
    189views Programming Languages» more  JMLR 2012»
    Universal Measurement Bounds for Structured Sparse Signal Recovery
    11 years 9 months ago
    Universal Measurement Bounds for Structured Sparse Signal Recovery
    Download  homepages.cae.wisc.edu
    Standard compressive sensing results state that to exactly recover an s sparse signal in Rp , one requires O(s · log p) measurements. While this bound is extremely useful in prac...
    Nikhil S. Rao, Ben Recht, Robert D. Nowak
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