KullbackLeibler divergence

Results: 54



#Item
21JMLR: Workshop and Conference Proceedings vol 40:1–35, 2015  On Learning Distributions from their Samples Sudeep Kamath  SUKAMATH @ PRINCETON . EDU

JMLR: Workshop and Conference Proceedings vol 40:1–35, 2015 On Learning Distributions from their Samples Sudeep Kamath SUKAMATH @ PRINCETON . EDU

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Source URL: jmlr.org

Language: English - Date: 2015-07-20 20:08:36
22Sparse Learning of Markovian Population Models in Random Environments Christoph Zechner1 , Federico Wadehn1 , and Heinz Koeppl1,2,3 1  arXiv:1401.4026v1 [q-bio.QM] 16 Jan 2014

Sparse Learning of Markovian Population Models in Random Environments Christoph Zechner1 , Federico Wadehn1 , and Heinz Koeppl1,2,3 1 arXiv:1401.4026v1 [q-bio.QM] 16 Jan 2014

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Source URL: arxiv.org

Language: English - Date: 2014-01-16 20:42:47
23Algorithms for Non-negative Matrix Factorization Daniel D. Lee* *BelJ Laboratories Lucent Technologies

Algorithms for Non-negative Matrix Factorization Daniel D. Lee* *BelJ Laboratories Lucent Technologies

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Source URL: papers.nips.cc

Language: English - Date: 2014-04-15 02:01:26
24Deep neural network context embeddings for model selection in rich-context HMM synthesis Thomas Merritt1 , Junichi Yamagishi1,2 , Zhizheng Wu1 , Oliver Watts1 , Simon King1 1  The Centre for Speech Technology Research, U

Deep neural network context embeddings for model selection in rich-context HMM synthesis Thomas Merritt1 , Junichi Yamagishi1,2 , Zhizheng Wu1 , Oliver Watts1 , Simon King1 1 The Centre for Speech Technology Research, U

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Source URL: www.cstr.inf.ed.ac.uk

Language: English - Date: 2015-09-29 11:06:25
25How biased are maximum entropy models?  Jakob H. Macke Gatsby Computational Neuroscience Unit University College London, UK

How biased are maximum entropy models? Jakob H. Macke Gatsby Computational Neuroscience Unit University College London, UK

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Source URL: www.mackelab.org

Language: English - Date: 2016-08-04 15:02:45
26Bayesian Learning Using Automatic Relevance Determination Prior with an Application to Earthquake Early Warning Chang Kook Oh1; James L. Beck2; and Masumi Yamada3 Abstract: A novel method of Bayesian learning with automa

Bayesian Learning Using Automatic Relevance Determination Prior with an Application to Earthquake Early Warning Chang Kook Oh1; James L. Beck2; and Masumi Yamada3 Abstract: A novel method of Bayesian learning with automa

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Source URL: www.eqh.dpri.kyoto-u.ac.jp

Language: English - Date: 2010-06-15 00:37:08
27INVITED PAPER Dynamic and Succinct Statistical Analysis of Neuroscience Data

INVITED PAPER Dynamic and Succinct Statistical Analysis of Neuroscience Data

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Source URL: coleman.ucsd.edu

Language: English
28Deformable Template As Active Basis Ying Nian Wu, Zhangzhang Si, Chuck Fleming, and Song-Chun Zhu UCLA Department of Statistics http://www.stat.ucla.edu/∼ywu/ActiveBasis.html  Abstract

Deformable Template As Active Basis Ying Nian Wu, Zhangzhang Si, Chuck Fleming, and Song-Chun Zhu UCLA Department of Statistics http://www.stat.ucla.edu/∼ywu/ActiveBasis.html Abstract

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Source URL: www.stat.ucla.edu

Language: English - Date: 2007-08-20 02:10:20
29Tsallis Kernels on Measures Andr´e F. T. Martins∗† ∗ Language Technologies Institute Carnegie Mellon University

Tsallis Kernels on Measures Andr´e F. T. Martins∗† ∗ Language Technologies Institute Carnegie Mellon University

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Source URL: users.isr.ist.utl.pt

Language: English - Date: 2007-11-25 10:58:18
30Which cliques? • In general, an undirected model can place potentials on any subset of the cliques of the graph. Lecture 11: Iterative Proportional Fitting

Which cliques? • In general, an undirected model can place potentials on any subset of the cliques of the graph. Lecture 11: Iterative Proportional Fitting

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Source URL: www.cs.nyu.edu

Language: English - Date: 2009-10-06 18:57:16