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Volumn 11, Issue , 2010, Pages 2855-2900

Expectation truncation and the benefits of preselection in training generative models

Author keywords

Component extraction; Deterministic approximations; Generative models; Maximum likelihood; Multiple cause models; Variational EM

Indexed keywords

COMPONENT EXTRACTION; DETERMINISTIC APPROXIMATION; GENERATIVE MODEL; MULTIPLE-CAUSE MODELS; VARIATIONAL EM;

EID: 78349238081     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (39)

References (40)
  • 1
    • 70349705656 scopus 로고    scopus 로고
    • A structured model of video reproduces primary visual cortical organisation
    • P. Berkes, R. E. Turner, and M. Sahani. A structured model of video reproduces primary visual cortical organisation. PLoS Computational Biology, 5 (9): el000495, 2009.
    • (2009) PLoS Computational Biology , vol.5 , Issue.9
    • Berkes, P.1    Turner, R.E.2    Sahani, M.3
  • 3
    • 33847391180 scopus 로고    scopus 로고
    • Learning sensory representations with intrinsic plasticity
    • N. J. Butko and J. Triesch. Learning sensory representations with intrinsic plasticity. Neurocomputing, 70(7-9):1130-1138, 2007.
    • (2007) Neurocomputing , vol.70 , Issue.7-9 , pp. 1130-1138
    • Butko, N.J.1    Triesch, J.2
  • 4
    • 0036707219 scopus 로고    scopus 로고
    • Unsupervised neural networks for the identification of minimum overcomplete basis in visual data
    • D. Charles, C. Fyfe, D. MacDonald, and J. Koetsier. Unsupervised neural networks for the identification of minimum overcomplete basis in visual data. Neurocomputing, 47(1-4):119-143, 2002.
    • (2002) Neurocomputing , vol.47 , Issue.1-4 , pp. 119-143
    • Charles, D.1    Fyfe, C.2    MacDonald, D.3    Koetsier, J.4
  • 5
    • 0028416938 scopus 로고
    • Independent component analysis, a new concept?
    • P. Comon. Independent component analysis, a new concept? Signal Processing, 36(3):287-314, 1994.
    • (1994) Signal Processing , vol.36 , Issue.3 , pp. 287-314
    • Comon, P.1
  • 6
    • 0001179408 scopus 로고
    • Competition and multiple cause models
    • P. Dayan and R. S. Zemel. Competition and multiple cause models. Neural Computation, 7:565-579, 1995.
    • (1995) Neural. Computation , vol.7 , pp. 565-579
    • Dayan, P.1    Zemel, R.S.2
  • 8
    • 0025604930 scopus 로고
    • Forming sparse representations by local anti-Hebbian learning
    • P. Földiák. Forming sparse representations by local anti-Hebbian learning. Biological Cybernetics, 64:165-170, 1990.
    • (1990) Biological Cybernetics , vol.64 , pp. 165-170
    • Földiák, P.1
  • 10
    • 0029652445 scopus 로고
    • The 'wake-sleep' algorithm for unsupervised neural networks
    • G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal. The 'wake-sleep' algorithm for unsupervised neural networks. Science, 268:1158-1161, 1995.
    • (1995) Science , vol.268 , pp. 1158-1161
    • Hinton, G.E.1    Dayan, P.2    Frey, B.J.3    Neal, R.M.4
  • 14
    • 4043084564 scopus 로고    scopus 로고
    • Tutorial on variational approximation methods
    • M. Opper and D. Saad, editors, MIT Press
    • T. Jaakkola. Tutorial on variational approximation methods. In M. Opper and D. Saad, editors, Advanced mean field methods: theory and practice. MIT Press, 2000.
    • (2000) Advanced Mean Field Methods: Theory and Practice
    • Jaakkola, T.1
  • 15
    • 0033225865 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. Machine Learning, 37(2):183-233, 1999.
    • (1999) Machine Learning , vol.37 , Issue.2 , pp. 183-233
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.S.3    Saul, L.K.4
  • 18
    • 0347304330 scopus 로고    scopus 로고
    • Selecting the k largest elements with parity tests
    • T. W. Lam and H.-F. Ting. Selecting the k largest elements with parity tests. Discrete Appl. Math., 101(1-3):187-196, 2000.
    • (2000) Discrete Appl. Math. , vol.101 , Issue.1-3 , pp. 187-196
    • Lam, T.W.1    Ting, H.-F.2
  • 19
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788-91, 1999.
    • (1999) Nature , vol.401 , Issue.6755 , pp. 788-791
    • Lee, D.D.1    Seung, H.S.2
  • 21
    • 0042565834 scopus 로고    scopus 로고
    • Hierarchical Bayesian inference in the visual cortex
    • T. S. Lee and D. Mumford. Hierarchical Bayesian inference in the visual cortex. J Opt Soc Am A Opt Image Sci Vis, 20(7):1434-1448, 2003.
    • (2003) J. Opt. Soc. Am. A Opt. Image Sci. Vis. , vol.20 , Issue.7 , pp. 1434-1448
    • Lee, T.S.1    Mumford, D.2
  • 22
    • 9144241266 scopus 로고    scopus 로고
    • Hierarchical self-organization of minicolumnar receptive fields
    • DOI 10.1016/j.neunet.2004.07.008, PII S0893608004001674, New Developments in Self-Organizing Systems
    • J. Lücke. Hierarchical self-organization of minicolumnar receptive fields. Neural Networks, 17/8-9:1377-1389, 2004. (Pubitemid 39539548)
    • (2004) Neural Networks , vol.17 , Issue.8-9 , pp. 1377-1389
    • Lucke, J.1
  • 24
    • 46749096794 scopus 로고    scopus 로고
    • Maximal causes for non-linear component extraction
    • J. Lücke and M. Sahani. Maximal causes for non-linear component extraction. Journal of Machine Learning Research, 9:1227-1267, 2008.
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 1227-1267
    • Lücke, J.1    Sahani, M.2
  • 25
    • 10744227912 scopus 로고    scopus 로고
    • Rapid processing and unsupervised learning in a model of the cortical macrocolumn
    • J. Lücke and C. von der Malsburg. Rapid processing and unsupervised learning in a model of the cortical macrocolumn. Neural Computation, 16:501-533, 2004.
    • (2004) Neural. Computation , vol.16 , pp. 501-533
    • Lücke, J.1    Von Malsburg, C.D.2
  • 26
    • 84858716136 scopus 로고    scopus 로고
    • Occlusive components analysis
    • Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors
    • J. Lücke, R. Turner, M. Sahani, and M. Henniges. Occlusive components analysis. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 1069-1077, 2009.
    • (2009) Advances in Neural. Information Processing Systems , vol.22 , pp. 1069-1077
    • Lücke, J.1    Turner, R.2    Sahani, M.3    Henniges, M.4
  • 28
    • 0345978970 scopus 로고    scopus 로고
    • Expectation propagation for approximate Bayesian inference
    • San Francisco, CA, USA, Morgan Kaufmann Publishers Inc. ISBN 1-55860-800-1
    • T. P. Minka. Expectation propagation for approximate Bayesian inference. In UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, pages 362-369, San Francisco, CA, USA, 2001. Morgan Kaufmann Publishers Inc. ISBN 1-55860-800-1.
    • (2001) UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence , pp. 362-369
    • Minka, T.P.1
  • 29
    • 0002788893 scopus 로고    scopus 로고
    • A view of the EM algorithm that justifies incremental, sparse, and other variants
    • M. I. Jordan, editor, Kluwer, y1998
    • R. Neal and G. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan, editor, Learning in Graphical Models. Kluwer, y1998.
    • Learning in Graphical Models
    • Neal, R.1    Hinton, G.2
  • 31
    • 0029938380 scopus 로고    scopus 로고
    • Emergence of simple-cell receptive field properties by learning a sparse code for natural images
    • B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381:607-609, 1996.
    • (1996) Nature , vol.381 , pp. 607-609
    • Olshausen, B.A.1    Field, D.J.2
  • 32
    • 0033316361 scopus 로고    scopus 로고
    • Hierarchical models of object recognition in cortex
    • M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nature Neuroscience, 211(11):1019-1025, 1999.
    • (1999) Nature Neuroscience , vol.211 , Issue.11 , pp. 1019-1025
    • Riesenhuber, M.1    Poggio, T.2
  • 34
    • 0003040479 scopus 로고
    • A multiple cause mixture model for unsupervised learning
    • E. Saund. A multiple cause mixture model for unsupervised learning. Neural Computation, 7:51-71, 1995.
    • (1995) Neural. Computation , vol.7 , pp. 51-71
    • Saund, E.1
  • 35
    • 33646697510 scopus 로고    scopus 로고
    • Learning image components for object recognition
    • M. W. Spratling. Learning image components for object recognition. Journal of Machine Learning Research, 7:793-815, 2006.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 793-815
    • Spratling, M.W.1
  • 37
    • 0032029288 scopus 로고    scopus 로고
    • Deterministic annealing EM algorithm
    • N. Ueda and R. Nakano. Deterministic annealing EM algorithm. Neural Networks, 11(2):271-282, 1998.
    • (1998) Neural. Networks , vol.11 , Issue.2 , pp. 271-282
    • Ueda, N.1    Nakano, R.2
  • 38
    • 0035380719 scopus 로고    scopus 로고
    • Rate coding versus temporal order coding: What the retinal ganglion cells tell the visual cortex
    • R. van Rullen and S. J. Thorpe. Rate coding versus temporal order coding: What the retinal ganglion cells tell the visual cortex. Neural Computation, 13(6):1255-1283, 2001.
    • (2001) Neural. Computation , vol.13 , Issue.6 , pp. 1255-1283
    • Van Rullen, R.1    Thorpe, S.J.2
  • 39
    • 68349132127 scopus 로고    scopus 로고
    • Combining feature-and correspondence-based methods for visual object recognition
    • G. Westphal and R. P. Würtz. Combining feature- and correspondence-based methods for visual object recognition. Neural Computation, 21(7):1952-1989, 2009.
    • (2009) Neural. Computation , vol.21 , Issue.7 , pp. 1952-1989
    • Westphal, G.1    Würtz, R.P.2
  • 40
    • 33746220445 scopus 로고    scopus 로고
    • Vision as Bayesian inference: Analysis by synthesis?
    • A. Yuille and D. Kersten. Vision as Bayesian inference: analysis by synthesis? Trends in Cognitive Sciences, 10(7):301-308, 2006.
    • (2006) Trends in Cognitive Sciences , vol.10 , Issue.7 , pp. 301-308
    • Yuille, A.1    Kersten, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.