Matching pursuit

Results: 58



#Item
11METISS Team The Matching Pursuit Tool Kit Getting Started Contributors:

METISS Team The Matching Pursuit Tool Kit Getting Started Contributors:

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Source URL: gforge.inria.fr

Language: English
    12CLASS PROJECT FOR EE225B (SPRING 2006), UNIVERSITY OF CALIFORNIA-BERKELEY 1 Sparse Signal Representation: Image Compression using Sparse Bayesian Learning

    CLASS PROJECT FOR EE225B (SPRING 2006), UNIVERSITY OF CALIFORNIA-BERKELEY 1 Sparse Signal Representation: Image Compression using Sparse Bayesian Learning

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    Source URL: www.ericbattenberg.com

    Language: English - Date: 2016-04-17 01:51:15
    13Orthogonal Matching Pursuit with Replacement Prateek Jain Microsoft Research India Bangalore, INDIA

    Orthogonal Matching Pursuit with Replacement Prateek Jain Microsoft Research India Bangalore, INDIA

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    Source URL: dept.stat.lsa.umich.edu

    Language: English - Date: 2012-09-12 18:50:23
    1412th International Society for Music Information Retrieval Conference (ISMIRFACTORIZATION OF OVERLAPPING HARMONIC SOUNDS USING APPROXIMATE MATCHING PURSUIT Steven K. Tjoa Imagine Research

    12th International Society for Music Information Retrieval Conference (ISMIRFACTORIZATION OF OVERLAPPING HARMONIC SOUNDS USING APPROXIMATE MATCHING PURSUIT Steven K. Tjoa Imagine Research

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    Source URL: ismir2011.ismir.net

    Language: English - Date: 2014-01-23 13:06:25
      15Lower Bounds for the Capture Time: Linear, Quadratic, and Beyond Klaus-Tycho F¨ orster, Rijad Nuridini, Jara Uitto, and Roger Wattenhofer Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerlan

      Lower Bounds for the Capture Time: Linear, Quadratic, and Beyond Klaus-Tycho F¨ orster, Rijad Nuridini, Jara Uitto, and Roger Wattenhofer Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerlan

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      Source URL: www.tik.ee.ethz.ch

      Language: English - Date: 2015-07-11 07:39:59
      16SIGNAL RECOVERY FROM RANDOM MEASUREMENTS VIA ORTHOGONAL MATCHING PURSUIT JOEL A. TROPP AND ANNA C. GILBERT Abstract. This article demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Match

      SIGNAL RECOVERY FROM RANDOM MEASUREMENTS VIA ORTHOGONAL MATCHING PURSUIT JOEL A. TROPP AND ANNA C. GILBERT Abstract. This article demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Match

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      Source URL: www.math.lsa.umich.edu

      Language: English - Date: 2012-08-02 12:49:37
        17Sulci Detection in Photos of the Human Cortex Based on Learned Discriminative Dictionaries Benjamin Berkels1 , Marc Kotowski2 , Martin Rumpf3 , and Carlo Schaller2 1 Interdisciplinary Mathematics Institute, University of

        Sulci Detection in Photos of the Human Cortex Based on Learned Discriminative Dictionaries Benjamin Berkels1 , Marc Kotowski2 , Martin Rumpf3 , and Carlo Schaller2 1 Interdisciplinary Mathematics Institute, University of

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        Source URL: numod.ins.uni-bonn.de

        Language: English - Date: 2011-07-15 10:07:10
        18Use of Learned Dictionaries in Tomographic Reconstruction Vincent Ettera , Ivana Jovanovi´cb,c and Martin Vetterlid a Laboratory for Communications and Applications, Ecole Polytechnique F´ed´erale de Lausanne, CH-1015

        Use of Learned Dictionaries in Tomographic Reconstruction Vincent Ettera , Ivana Jovanovi´cb,c and Martin Vetterlid a Laboratory for Communications and Applications, Ecole Polytechnique F´ed´erale de Lausanne, CH-1015

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        Source URL: vincent.etter.io

        Language: English - Date: 2013-11-29 04:12:17
        19Acoustic detection of multiple birds in environmental audio by Matching Pursuit Dan Stowell and Mark D. Plumbley Centre for Digital Music, Queen Mary, University of London

        Acoustic detection of multiple birds in environmental audio by Matching Pursuit Dan Stowell and Mark D. Plumbley Centre for Digital Music, Queen Mary, University of London

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        Source URL: c4dm.eecs.qmul.ac.uk

        Language: English - Date: 2013-06-17 15:25:26
        20Are Sparse Representations Rich Enough for Acoustic Modeling? Oriol Vinyals1 , Li Deng2 1 University of California at Berkeley (Berkeley, CA, US) 2

        Are Sparse Representations Rich Enough for Acoustic Modeling? Oriol Vinyals1 , Li Deng2 1 University of California at Berkeley (Berkeley, CA, US) 2

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        Source URL: www1.icsi.berkeley.edu

        Language: English - Date: 2012-08-20 14:23:22