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    ICML
    2009
    IEEE
    144views Machine Learning» more  ICML 2009»
    A Bayesian approach to protein model quality assessment
    14 years 7 months ago
    A Bayesian approach to protein model quality assessment
    Download  www.cs.cmu.edu
    Given multiple possible models b1, b2, . . . bn for a protein structure, a common sub-task in in-silico Protein Structure Prediction is ranking these models according to their qua...
    Hetunandan Kamisetty, Christopher James Langmead
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    NIPS
    2001
    185views Information Technology» more  NIPS 2001»
    Probabilistic principles in unsupervised learning of visual structure: human data and a model
    13 years 8 months ago
    Probabilistic principles in unsupervised learning of visual structure: human data and a model
    Download  www.physics.brown.edu
    To find out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images wit...
    Shimon Edelman, Benjamin P. Hiles, Hwajin Yang, Na...
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    TNN
    2008
    178views more  TNN 2008»
    IMORL: Incremental Multiple-Object Recognition and Localization
    13 years 6 months ago
    IMORL: Incremental Multiple-Object Recognition and Localization
    Download  www.csse.monash.edu.au
    This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
    Haibo He, Sheng Chen
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    GECCO
    2009
    Springer
    204views Optimization» more  GECCO 2009»
    Combined structure and motion extraction from visual data using evolutionary active learning
    13 years 11 months ago
    Combined structure and motion extraction from visual data using evolutionary active learning
    Download  www.cs.uvm.edu
    We present a novel stereo vision modeling framework that generates approximate, yet physically-plausible representations of objects rather than creating accurate models that are c...
    Krishnanand N. Kaipa, Josh C. Bongard, Andrew N. M...
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    BIOINFORMATICS
    2006
    105views more  BIOINFORMATICS 2006»
    Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure
    13 years 6 months ago
    Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure
    Download  noble.gs.washington.edu
    Darrin P. Lewis, Tony Jebara, William Stafford Nob...
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