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    TCAD
    2010
    164views more  TCAD 2010»
    Advanced Variance Reduction and Sampling Techniques for Efficient Statistical Timing Analysis
    14 years 4 months ago
    Advanced Variance Reduction and Sampling Techniques for Efficient Statistical Timing Analysis
    Download  www.vlsi.uwaterloo.ca
    The Monte-Carlo (MC) technique is a traditional solution for a reliable statistical analysis, and in contrast to probabilistic methods, it can account for any complicate model. How...
    Javid Jaffari, Mohab Anis
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    UAI
    2004
    114views Artificial Intelligence» more  UAI 2004»
    On-line Prediction with Kernels and the Complexity Approximation Principle
    14 years 11 months ago
    On-line Prediction with Kernels and the Complexity Approximation Principle
    Download  uai.sis.pitt.edu
    The paper describes an application of Aggregating Algorithm to the problem of regression. It generalizes earlier results concerned with plain linear regression to kernel technique...
    Alexander Gammerman, Yuri Kalnishkan, Vladimir Vov...
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    SIGGRAPH
    1995
    ACM
    180views Computer Graphics» more  SIGGRAPH 1995»
    Optimally combining sampling techniques for Monte Carlo rendering
    15 years 1 months ago
    Optimally combining sampling techniques for Monte Carlo rendering
    Download  www.ics.uci.edu
    Monte Carlo integration is a powerful technique for the evaluation of difficult integrals. Applications in rendering include distribution ray tracing, Monte Carlo path tracing, a...
    Eric Veach, Leonidas J. Guibas
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    ECCV
    2008
    Springer
    168views Computer Vision» more  ECCV 2008»
    A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
    15 years 11 months ago
    A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
    Download  gamma.cs.unc.edu
    The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction o...
    Rahul Raguram, Jan-Michael Frahm, Marc Pollefeys
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    ACL
    2004
    104views Computational Linguistics» more  ACL 2004»
    A Kernel PCA Method for Superior Word Sense Disambiguation
    14 years 11 months ago
    A Kernel PCA Method for Superior Word Sense Disambiguation
    Download  www.cs.ust.hk
    We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy superior to the best pu...
    Dekai Wu, Weifeng Su, Marine Carpuat
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