Hyperparameter

Results: 60



#Item
1Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization Lisha Li

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization Lisha Li

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Source URL: arxiv.org

- Date: 2016-11-23 20:48:17
    2Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures J. Bergstra Rowland Institute at Harvard 100 Edwin H. Land Boulevard Cambridge, MA 02142, USA

    Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures J. Bergstra Rowland Institute at Harvard 100 Edwin H. Land Boulevard Cambridge, MA 02142, USA

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    Source URL: jmlr.org

    - Date: 2013-08-14 01:36:42
      3An Efficient Approach for Assessing Hyperparameter Importance  Frank Hutter University of Freiburg, Freiburg, GERMANY  FH @ INFORMATIK . UNI - FREIBURG . DE

      An Efficient Approach for Assessing Hyperparameter Importance Frank Hutter University of Freiburg, Freiburg, GERMANY FH @ INFORMATIK . UNI - FREIBURG . DE

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      Source URL: www.cs.ubc.ca

      - Date: 2014-01-11 02:51:41
        4Efficient and Robust Automated Machine Learning  Matthias Feurer Aaron Klein Katharina Eggensperger Jost Tobias Springenberg

        Efficient and Robust Automated Machine Learning Matthias Feurer Aaron Klein Katharina Eggensperger Jost Tobias Springenberg

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        Source URL: papers.nips.cc

        Language: English - Date: 2016-01-30 21:34:03
        5Despite the popularity and superior performance of Gaussian-kernel support vector machine (SVM), the two hyperparameters sigma (scale) and C (tradeoff) remain hard to be tuned. Many techniques have been developed to addr

        Despite the popularity and superior performance of Gaussian-kernel support vector machine (SVM), the two hyperparameters sigma (scale) and C (tradeoff) remain hard to be tuned. Many techniques have been developed to addr

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        Source URL: mmds-data.org

        Language: English - Date: 2016-06-23 15:50:48
        6Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool arXiv:1607.08878v1 [cs.NE] 29 Jul 2016

        Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool arXiv:1607.08878v1 [cs.NE] 29 Jul 2016

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        Source URL: arxiv.org

        Language: English - Date: 2016-07-31 20:28:45
        7Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves

        Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves

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        Source URL: ijcai.org

        Language: English - Date: 2016-02-13 07:15:40
        8September 14, :56 WSPC - Proceedings Trim Size: 9.75in x 6.5in

        September 14, :56 WSPC - Proceedings Trim Size: 9.75in x 6.5in

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        Source URL: www.jsbi.org

        Language: English - Date: 2009-12-21 01:59:54
        9output/maye11bayesian.dvi

        output/maye11bayesian.dvi

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        Source URL: europa.informatik.uni-freiburg.de

        Language: English - Date: 2012-02-24 09:20:37
        10326  IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 17, NO. 3, MARCH 2008 Parameter Estimation in TV Image Restoration Using Variational Distribution Approximation

        326 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 17, NO. 3, MARCH 2008 Parameter Estimation in TV Image Restoration Using Variational Distribution Approximation

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        Source URL: decsai.ugr.es

        Language: English - Date: 2008-02-28 03:58:00