Tucker decomposition

Results: 11



#Item
1PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION Yuki Takashima1 , Hajime Yano1 , Toru Nakashika2 , Tetsuya Takiguchi1 , Yasuo Ariki1 1 2

PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION Yuki Takashima1 , Hajime Yano1 , Toru Nakashika2 , Tetsuya Takiguchi1 , Yasuo Ariki1 1 2

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Source URL: www.me.cs.scitec.kobe-u.ac.jp

Language: English - Date: 2018-05-02 02:05:54
    2Discovering Facts with Boolean Tensor Tucker Decomposition ˝ ∗ Dóra Erdos Pauli Miettinen

    Discovering Facts with Boolean Tensor Tucker Decomposition ˝ ∗ Dóra Erdos Pauli Miettinen

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    Source URL: people.mpi-inf.mpg.de

    - Date: 2013-09-03 10:48:48
      3Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis Zenglin Xu, Feng Yan {XU 218, YAN 12}@ PURDUE . EDU Department of Computer Science, Purdue University, West Lafayette, INUSA

      Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis Zenglin Xu, Feng Yan {XU 218, YAN 12}@ PURDUE . EDU Department of Computer Science, Purdue University, West Lafayette, INUSA

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      Source URL: www.cs.purdue.edu

      Language: English - Date: 2012-10-05 16:24:17
        4Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations Aaron Schein University of Massachusetts Amherst Mingyuan Zhou University of Texas at Austin

        Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations Aaron Schein University of Massachusetts Amherst Mingyuan Zhou University of Texas at Austin

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        Source URL: dirichlet.net

        Language: English - Date: 2016-05-27 14:35:50
          5Quotes from∗ Fundamentals of Computing II: Abstraction, Data Structures, and Large Software Systems Allen B. Tucker Robert D. Cupper

          Quotes from∗ Fundamentals of Computing II: Abstraction, Data Structures, and Large Software Systems Allen B. Tucker Robert D. Cupper

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          Source URL: www.win.tue.nl

          Language: English - Date: 2010-09-07 04:16:35
          6Efficient Numerical Methods for Least-Norm Regularization D.C. Sorensen I

          Efficient Numerical Methods for Least-Norm Regularization D.C. Sorensen I

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          Source URL: www.caam.rice.edu

          Language: English - Date: 2010-05-20 09:39:55
          7Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis Zenglin Xu, Feng Yan {XU 218, YAN 12}@ PURDUE . EDU Department of Computer Science, Purdue University, West Lafayette, IN[removed]USA

          Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis Zenglin Xu, Feng Yan {XU 218, YAN 12}@ PURDUE . EDU Department of Computer Science, Purdue University, West Lafayette, IN[removed]USA

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          Source URL: icml.cc

          Language: English - Date: 2012-06-07 13:20:16
          8Three-way analysis New multi-way models and algorithms for solving blind source separation problems Rasmus Bro Chemometrics Group, Food Technology Royal Veterinary & Agricultural University (KVL)

          Three-way analysis New multi-way models and algorithms for solving blind source separation problems Rasmus Bro Chemometrics Group, Food Technology Royal Veterinary & Agricultural University (KVL)

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          Source URL: www.irisa.fr

          Language: English - Date: 2001-07-17 11:10:18
          9Optimal Real-Time Filters for Linear Prediction Problems Marc Wildi∗and Tucker McElroy† U.S. Census Bureau Abstract The classic model-based paradigm in time series analysis is rooted in the Wold decomposition

          Optimal Real-Time Filters for Linear Prediction Problems Marc Wildi∗and Tucker McElroy† U.S. Census Bureau Abstract The classic model-based paradigm in time series analysis is rooted in the Wold decomposition

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          Source URL: blog.zhaw.ch

          Language: English - Date: 2013-12-27 12:09:55
          10Equivariant and scale-free Tucker decomposition models Peter David Hoff1 Working Paper no. 142 Center for Statistics and the Social Sciences University of Washington Seattle, WA[removed]

          Equivariant and scale-free Tucker decomposition models Peter David Hoff1 Working Paper no. 142 Center for Statistics and the Social Sciences University of Washington Seattle, WA[removed]

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          Source URL: www.csss.washington.edu

          Language: English - Date: 2013-12-31 13:11:39