Gaussian

Results: 2017



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91Gaussian Process Regression Networks Andrew Gordon Wilson mlg.eng.cam.ac.uk/andrew University of Cambridge

Gaussian Process Regression Networks Andrew Gordon Wilson mlg.eng.cam.ac.uk/andrew University of Cambridge

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Source URL: www.cs.cmu.edu

- Date: 2014-09-11 09:47:07
    92Student-t Processes as Alternatives to Gaussian Processes Amar Shah University of Cambridge Andrew Gordon Wilson

    Student-t Processes as Alternatives to Gaussian Processes Amar Shah University of Cambridge Andrew Gordon Wilson

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    Source URL: www.cs.cmu.edu

    - Date: 2014-09-11 09:47:10
      93Journal of Machine Learning Research2078 Submitted 8/07; Revised 4/08; PublishedApproximations for Binary Gaussian Process Classification Hannes Nickisch

      Journal of Machine Learning Research2078 Submitted 8/07; Revised 4/08; PublishedApproximations for Binary Gaussian Process Classification Hannes Nickisch

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      Source URL: jmlr.csail.mit.edu

      - Date: 2008-10-08 18:55:09
        94Active Learning with Gaussian Processes for Object Categorization Ashish Kapoor Microsoft Research Redmond, WA 98052, USA Kristen Grauman

        Active Learning with Gaussian Processes for Object Categorization Ashish Kapoor Microsoft Research Redmond, WA 98052, USA Kristen Grauman

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        Source URL: people.csail.mit.edu

        - Date: 2007-08-21 07:34:21
          95Gaussian Process Kernels for Pattern Discovery and Extrapolation Supplementary Material Andrew Gordon Wilson and Ryan Prescott Adams 1

          Gaussian Process Kernels for Pattern Discovery and Extrapolation Supplementary Material Andrew Gordon Wilson and Ryan Prescott Adams 1

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          Source URL: jmlr.org

          - Date: 2013-08-14 01:36:45
            96Chapter 1 Additive Gaussian Processes Section 1.7 showed how to learn the structure of a kernel by building it up piece-bypiece. This chapter presents an alternative approach: starting with many different types of struct

            Chapter 1 Additive Gaussian Processes Section 1.7 showed how to learn the structure of a kernel by building it up piece-bypiece. This chapter presents an alternative approach: starting with many different types of struct

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            Source URL: raw.githubusercontent.com

              97Journal of Machine Learning Research–1704 Submitted 8/05; PublishedAssessing Approximate Inference for Binary Gaussian Process Classification

              Journal of Machine Learning Research–1704 Submitted 8/05; PublishedAssessing Approximate Inference for Binary Gaussian Process Classification

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              Source URL: www.jmlr.org

              - Date: 2005-10-02 10:31:59
                98Student-t Processes as Alternatives to Gaussian Processes Supplementary Material In Appendix 1, we provide proofs of Lemmas and Corollaries from our paper. We describe the derivatives of the log marginal likelihood of t

                Student-t Processes as Alternatives to Gaussian Processes Supplementary Material In Appendix 1, we provide proofs of Lemmas and Corollaries from our paper. We describe the derivatives of the log marginal likelihood of t

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                Source URL: www.cs.cmu.edu

                - Date: 2014-09-11 09:47:11
                  992016 IEEE 57th Annual Symposium on Foundations of Computer Science Approximate Gaussian Elimination for Laplacians – Fast, Sparse, and Simple Rasmus Kyng, Sushant Sachdeva Department of Computer Science

                  2016 IEEE 57th Annual Symposium on Foundations of Computer Science Approximate Gaussian Elimination for Laplacians – Fast, Sparse, and Simple Rasmus Kyng, Sushant Sachdeva Department of Computer Science

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                  Source URL: ieee-focs.org

                  - Date: 2016-10-06 00:33:48
                    100Gaussian Random Number Generators DAVID B. THOMAS and WAYNE LUK 11 Imperial College

                    Gaussian Random Number Generators DAVID B. THOMAS and WAYNE LUK 11 Imperial College

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                    Source URL: www.doc.ic.ac.uk

                    - Date: 2009-12-13 12:02:23