Latent variable

Results: 377



#Item
1A Discriminative Latent Variable Chinese Segmenter with Hybrid Word/Character Information Xu Sun Department of Computer Science University of Tokyo

A Discriminative Latent Variable Chinese Segmenter with Hybrid Word/Character Information Xu Sun Department of Computer Science University of Tokyo

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Source URL: aclweb.org

Language: English - Date: 2010-06-14 21:09:34
    2The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling Shengjia Zhao Stanford University

    The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling Shengjia Zhao Stanford University

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    Source URL: bayesiandeeplearning.org

    Language: English - Date: 2017-12-05 15:02:31
      3Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models David M. Blei Columbia University Abstract

      Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models David M. Blei Columbia University Abstract

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      Source URL: www.cs.columbia.edu

      Language: English - Date: 2016-10-03 10:35:09
        4Technical Report: University of Edinburgh, Latent-Variable MDP Models for Adapting the Interaction Environment of Diverse Users Subramanian Ramamoorthy M. M. Hassan Mahmud

        Technical Report: University of Edinburgh, Latent-Variable MDP Models for Adapting the Interaction Environment of Diverse Users Subramanian Ramamoorthy M. M. Hassan Mahmud

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        Source URL: www.benjaminrosman.com

        Language: English - Date: 2014-05-07 16:55:53
          5Psychology 454: Psychological Measurement An introduction to latent variable modeling William Revelle Swift 315 email: November 21, 2016

          Psychology 454: Psychological Measurement An introduction to latent variable modeling William Revelle Swift 315 email: November 21, 2016

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          Source URL: www.personality-project.org

          Language: English - Date: 2016-11-21 12:28:33
            6Variational EM Algorithms for Non-Gaussian Latent Variable Models J. A. Palmer, K. Kreutz-Delgado, D. P. Wipf, and B. D. Rao Department of Electrical and Computer Engineering University of California San Diego, La Jolla,

            Variational EM Algorithms for Non-Gaussian Latent Variable Models J. A. Palmer, K. Kreutz-Delgado, D. P. Wipf, and B. D. Rao Department of Electrical and Computer Engineering University of California San Diego, La Jolla,

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            Source URL: dsp.ucsd.edu

            Language: English - Date: 2018-07-20 20:47:10
              7Self-Paced Learning for Latent Variable Models M. Pawan Kumar Ben Packer Daphne Koller Computer Science Department

              Self-Paced Learning for Latent Variable Models M. Pawan Kumar Ben Packer Daphne Koller Computer Science Department

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              Source URL: robotics.stanford.edu

              Language: English - Date: 2010-10-31 15:19:37
                8Journal of Machine Learning ResearchSubmitted 8/15; Revised 6/16; PublishedIntegrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses

                Journal of Machine Learning ResearchSubmitted 8/15; Revised 6/16; PublishedIntegrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses

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                Source URL: jmlr.csail.mit.edu

                Language: English - Date: 2017-07-22 15:42:28
                  9Dynamical Binary Latent Variable Models for 3D Human Pose Tracking Graham W. Taylor New York University New York, USA Leonid Sigal

                  Dynamical Binary Latent Variable Models for 3D Human Pose Tracking Graham W. Taylor New York University New York, USA Leonid Sigal

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                  Source URL: www.cs.toronto.edu

                  - Date: 2010-06-05 20:48:29
                    10Chapter 2 Graphical models and approximate posterior inference In this chapter we review latent variable graphical models and exponential families. We discuss variational methods and Gibbs sampling for approximate poster

                    Chapter 2 Graphical models and approximate posterior inference In this chapter we review latent variable graphical models and exponential families. We discuss variational methods and Gibbs sampling for approximate poster

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                    Source URL: www.cs.princeton.edu

                    - Date: 2006-03-11 08:59:30