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Volumn 12, Issue , 2011, Pages 1025-1068

Two distributed-state models for generating high-dimensional time series

Author keywords

Generative models; Motion capture; Restricted Boltzmann machines; Time series; Unsupervised learning

Indexed keywords

COMPUTATIONAL PROPERTIES; DYNAMIC STATE; EFFECTIVE INTERACTIONS; FILLING IN; GENERATIVE MODEL; HIGH-DIMENSIONAL; LATENT VARIABLE; LEARNING PROCEDURES; MODEL-BASED; MOTION CAPTURE; MOTION CAPTURE DATA; MOTION STYLES; RESTRICTED BOLTZMANN MACHINE; SOURCE CODES; TIME STEP;

EID: 79955836081     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (110)

References (72)
  • 1
    • 0001578518 scopus 로고
    • A learning algorithm for Boltzmann machines
    • D. H. Ackley, G. E. Hinton, and T. J. Sejnowski. A learning algorithm for Boltzmann machines. Cognitive Science, 9(1): 147-169, 1985.
    • (1985) Cognitive Science , vol.9 , Issue.1 , pp. 147-169
    • Ackley, D.H.1    Hinton, G.E.2    Sejnowski, T.J.3
  • 4
    • 67651049775 scopus 로고    scopus 로고
    • Justifying and generalizing contrastive divergence
    • Y. Bengio and O. Delalleau. Justifying and generalizing contrastive divergence. Neural Computation, 21(1): 1-21, 2008.
    • (2008) Neural Computation , vol.21 , Issue.1 , pp. 1-21
    • Bengio, Y.1    Delalleau, O.2
  • 6
    • 24644473647 scopus 로고    scopus 로고
    • Modeling and learning contact dynamics in human motion
    • Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
    • A. Bissacco. Modeling and learning contact dynamics in human motion. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pages 421-428. IEEE, 2005. (Pubitemid 41275972)
    • (2005) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , vol.1 , pp. 421-428
    • Bissacco, A.1
  • 9
    • 0345368881 scopus 로고
    • Unsupervised learning of distributions of binary vectors using 2-layer networks
    • J. Moody, S. H. Hanson, and R. Lippmann, editors. Morgan-Kaufmann
    • Y. Freund and D. Haussler. Unsupervised learning of distributions of binary vectors using 2-layer networks. In J. Moody, S. H. Hanson, and R. Lippmann, editors, Advances in Neural Information Processing Systems (NIPS 4): Proceedings of the 1991 Conference, pages 912-919. Morgan-Kaufmann, 1992.
    • (1992) Advances in Neural Information Processing Systems (NIPS 4): Proceedings of the 1991 Conference , pp. 912-919
    • Freund, Y.1    Haussler, D.2
  • 11
    • 0002049440 scopus 로고    scopus 로고
    • Learning dynamic Bayesian networks
    • Adaptive Processing of Sequences and Data Structures
    • Z. Ghahramani. Learning dynamic Bayesian networks. In C. Giles and M. Gori, editors, Adaptive Processing of Sequences and Data Structures, pages 168-197. Springer-Verlag, Berlin, 1998. (Pubitemid 128056031)
    • (1998) Lecture Notes in Computer Science , Issue.1387 , pp. 168-197
    • Ghahramani, Z.1
  • 12
    • 0002321764 scopus 로고    scopus 로고
    • Practical parameterization of rotations using the exponential map
    • F. S. Grassia. Practical parameterization of rotations using the exponential map. Journal of Graphics Tools, 3(3):29-48, 1998.
    • (1998) Journal of Graphics Tools , vol.3 , Issue.3 , pp. 29-48
    • Grassia, F.S.1
  • 13
    • 0013344078 scopus 로고    scopus 로고
    • Training products of experts by minimizing contrastive divergence
    • G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771-1800, 2002.
    • (2002) Neural Computation , vol.14 , Issue.8 , pp. 1771-1800
    • Hinton, G.E.1
  • 14
    • 35348818718 scopus 로고    scopus 로고
    • Learning multiple layers of representation
    • DOI 10.1016/j.tics.2007.09.004, PII S1364661307002173
    • G. E. Hinton. Learning multiple layers of representation. Trends in Cognitive Sciences, 11(10):428-434, 2007. (Pubitemid 47588994)
    • (2007) Trends in Cognitive Sciences , vol.11 , Issue.10 , pp. 428-434
    • Hinton, G.E.1
  • 17
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • DOI 10.1126/science.1127647
    • G. E. Hinton and R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507, 2006. (Pubitemid 44148451)
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 18
    • 0029652445 scopus 로고
    • The wake-sleep algorithm for self-organizing neural networks
    • G. E. Hinton, P. Dayan, B. J. Frey, and R. Neal. The wake-sleep algorithm for self-organizing 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.4
  • 19
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • DOI 10.1162/neco.2006.18.7.1527
    • G. E. Hinton, S. Osindero, and Y. W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006. (Pubitemid 44024729)
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.-W.3
  • 21
    • 22044434800 scopus 로고    scopus 로고
    • Estimation of non-normalized statistical models by score matching
    • ISSN 1532-4435
    • A. Hyvärinen. Estimation of non-normalized statistical models by score matching. Journal of Machine Learning Research, 6:695-709, 2005. ISSN 1532-4435.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 695-709
    • Hyvärinen, A.1
  • 25
    • 84898980901 scopus 로고    scopus 로고
    • Gaussian process latent variable models for visualisation of high dimensional data
    • S. Thrun, L. Saul, and B. Schölkopf, editors., MIT Press
    • N. D. Lawrence. Gaussian process latent variable models for visualisation of high dimensional data. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems (NIPS 16): Proceedings of the 2003 Conference, pages 329-326. MIT Press, 2004.
    • (2004) Advances in Neural Information Processing Systems (NIPS 16): Proceedings of the 2003 Conference , pp. 329-326
    • Lawrence, N.D.1
  • 29
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
    • (1998) Proceedings of the IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • Lecun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 30
    • 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):788-791, 1999.
    • (1999) Nature , vol.401 , Issue.6755 , pp. 788-791
    • Lee, D.D.1    Seung, H.S.2
  • 35
    • 0026858102 scopus 로고
    • Noise injection into inputs in back-propagation learning
    • K. Matsuoka. Noise injection into inputs in back-propagation learning. IEEE Trans. on Systems, Man, and Cybernetics, 22(3):436-440, 1992.
    • (1992) IEEE Trans. on Systems, Man, and Cybernetics , vol.22 , Issue.3 , pp. 436-440
    • Matsuoka, K.1
  • 37
    • 77953520240 scopus 로고    scopus 로고
    • Learning to represent spatial transformations with factored higher-order boltzmann machines
    • R. Memisevic and G. E. Hinton. Learning to represent spatial transformations with factored higher-order boltzmann machines. Neural Computation, 22:1473-1492, 2010.
    • (2010) Neural Computation , vol.22 , pp. 1473-1492
    • Memisevic, R.1    Hinton, G.E.2
  • 41
    • 78149306047 scopus 로고    scopus 로고
    • 3-d object recognition with deep belief nets
    • MIT Press, Cambridge, MA
    • V. Nair and G. E. Hinton. 3-d object recognition with deep belief nets. In Advances in Neural Information Processing Systems 22, pages 1339-1347. MIT Press, Cambridge, MA, 2009.
    • (2009) Advances in Neural Information Processing Systems , vol.22 , pp. 1339-1347
    • Nair, V.1    Hinton, G.E.2
  • 42
    • 0002788893 scopus 로고    scopus 로고
    • A view of the em algorithm that justifies incremental, sparse, and other variants
    • M. I. Jordan, editor, Kluwer Academic Publishers
    • R. M. Neal and G. E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan, editor, Learning in Graphical Models, pages 355-368. Kluwer Academic Publishers, 1998.
    • (1998) Learning in Graphical Models , pp. 355-368
    • Neal, R.M.1    Hinton, G.E.2
  • 43
    • 0030779611 scopus 로고    scopus 로고
    • Sparse coding with an overcomplete basis set: A strategy employed by V1?
    • DOI 10.1016/S0042-6989(97)00169-7, PII S0042698997001697
    • B. A. Olshausen and D. J. Field. Sparse coding with an overcomplete basis set: A strategy employed byV1? Vision Research, 37(23):3311-3325, 1997. (Pubitemid 27493805)
    • (1997) Vision Research , vol.37 , Issue.23 , pp. 3311-3325
    • Olshausen, B.A.1    Field, D.J.2
  • 55
    • 0000329993 scopus 로고
    • Information processing in dynamical systems: Foundations of harmony theory
    • D. E. Rumelhart J. L. McClelland et al. editors, MIT Press, Cambridge, MA
    • P. Smolensky. Information processing in dynamical systems: Foundations of harmony theory. In D. E. Rumelhart, J. L. McClelland, et al., editors, Parallel Distributed Processing: Volume 1: Foundations, pages 194-281. MIT Press, Cambridge, MA, 1986.
    • (1986) Parallel Distributed Processing: Volume 1: Foundations , pp. 194-281
    • Smolensky, P.1
  • 57
    • 55749105263 scopus 로고    scopus 로고
    • Deep narrow sigmoid belief networks are universal approximators
    • I. Sutskever and G. E. Hinton. Deep narrow sigmoid belief networks are universal approximators. Neural Computation, 20(11):2629-2636, 2008.
    • (2008) Neural Computation , vol.20 , Issue.11 , pp. 2629-2636
    • Sutskever, I.1    Hinton, G.E.2
  • 59
    • 84962171721 scopus 로고    scopus 로고
    • Realistic synthesis of novel human movements from a database of motion capture examples
    • IEEE Computer Society
    • L. Tanco and A. Hilton. Realistic synthesis of novel human movements from a database of motion capture examples. In Proceedings of the Workshop on Human Motion (HUMO '00), pages 137-142. IEEE Computer Society, 2000.
    • (2000) Proceedings of the Workshop on Human Motion (HUMO ' 00) , pp. 137-142
    • Tanco, L.1    Hilton, A.2
  • 63
    • 0034202338 scopus 로고    scopus 로고
    • Separating style and content with bilinear models
    • J. B. Tenenbaum and W. T. Freeman. Separating style and content with bilinear models. Neural Computation, 12(6):1247-1283, 2000.
    • (2000) Neural Computation , vol.12 , Issue.6 , pp. 1247-1283
    • Tenenbaum, J.B.1    Freeman, W.T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.