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    UAI
    2008
    234views Artificial Intelligence» more  UAI 2008»
    Improving the Accuracy and Efficiency of MAP Inference for Markov Logic
    13 years 5 months ago
    Improving the Accuracy and Efficiency of MAP Inference for Markov Logic
    Download  uai2008.cs.helsinki.fi
    In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori (MAP) inference method for Statistical Relational Learning. Framed in terms of Markov Logic and inspi...
    Sebastian Riedel
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    AAAI
    2008
    165views Intelligent Agents» more  AAAI 2008»
    Hybrid Markov Logic Networks
    13 years 6 months ago
    Hybrid Markov Logic Networks
    Download  www.cs.washington.edu
    Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent fram...
    Jue Wang, Pedro Domingos
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    AAAI
    2007
    103views Intelligent Agents» more  AAAI 2007»
    Mapping and Revising Markov Logic Networks for Transfer Learning
    13 years 6 months ago
    Mapping and Revising Markov Logic Networks for Transfer Learning
    Download  alchemy.cs.washington.edu
    Transfer learning addresses the problem of how to leverage knowledge acquired in a source domain to improve the accuracy and speed of learning in a related target domain. This pap...
    Lilyana Mihalkova, Tuyen N. Huynh, Raymond J. Moon...
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    CVPR
    2010
    IEEE
    287views Computer Vision» more  CVPR 2010»
    Ray Markov Random Fields for Image-Based 3D Modeling: Model and Efficient Inference
    13 years 7 months ago
    Ray Markov Random Fields for Image-Based 3D Modeling: Model and Efficient Inference
    Download  www.lems.brown.edu
    In this paper, we present an approach to multi-view image-based 3D reconstruction by statistically inversing the ray-tracing based image generation process. The proposed algorithm...
    Shubao Liu, David Cooper
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    CVPR
    2006
    IEEE
    216views Computer Vision» more  CVPR 2006»
    Solving Markov Random Fields using Second Order Cone Programming Relaxations
    14 years 6 months ago
    Solving Markov Random Fields using Second Order Cone Programming Relaxations
    Download  eprints.pascal-network.org
    This paper presents a generic method for solving Markov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solvi...
    M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserma...
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