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Volumn 32, Issue 2, 2015, Pages 131-139

An Overview of Deep Generative Models

Author keywords

Deep autoencoder; Deep belief networks; Deep boltzmann machine; Deep generative model; Restricted boltzmann machine

Indexed keywords

IMAGE PROCESSING; LEARNING SYSTEMS;

EID: 85085272778     PISSN: 02564602     EISSN: 09745971     Source Type: Journal    
DOI: 10.1080/02564602.2014.987328     Document Type: Review
Times cited : (60)

References (75)
  • 1
    • 0042565834 scopus 로고    scopus 로고
    • Hierarchical Bayesian inference in the visual cortex
    • Jul., and
    • T. S. Lee, and D. Mumford, “Hierarchical Bayesian inference in the visual cortex,” The Journal of the Optical Society of America A, Vol. 20, no. 7, pp. 1434–48, Jul. 2003.
    • (2003) The Journal of the Optical Society of America A , vol.20 , Issue.7 , pp. 1434-1448
    • Lee, T.S.1    Mumford, D.2
  • 3
    • 0031875590 scopus 로고    scopus 로고
    • The role of the primary visual cortex in higher level vision
    • Aug., and
    • T. S. Lee, D. Mumford, and R. Romero, “The role of the primary visual cortex in higher level vision,” Vision Research, Vol. 38, no. 15, pp. 2429–54, Aug. 1998.
    • (1998) Vision Research , vol.38 , Issue.15 , pp. 2429-2454
    • Lee, T.S.1    Mumford, D.2    Romero, R.3
  • 4
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Oct., and
    • D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, Vol. 323, no. 7, pp. 533–6, Oct. 1986.
    • (1986) Nature , vol.323 , Issue.7 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 5
    • 84864073449 scopus 로고    scopus 로고
    • Greedy layer-wise training of deep networks
    • Schölkopf B., Platt J.C., Hoffman T., (eds), Cambridge, MA: MIT Press, and, Eds
    • Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layer-wise training of deep networks,” in Advances in Neural Information Processing Systems, Vol. 19, B. Schölkopf, J. C. Platt and T. Hoffman, Eds. Cambridge, MA: MIT Press, 2006, pp. 153–60.
    • (2006) Advances in Neural Information Processing Systems , vol.19 , pp. 153-160
    • Bengio, Y.1    Lamblin, P.2    Popovici, D.3    Larochelle, H.4
  • 8
    • 0000783715 scopus 로고
    • Replicator neural networks for universal optimal source coding
    • Sept
    • R. Hecht-Nielsen, “Replicator neural networks for universal optimal source coding,” Science, Vol. 269, pp. 1860–3, Sept. 1995.
    • (1995) Science , vol.269 , pp. 1860-1863
    • Hecht-Nielsen, R.1
  • 9
    • 0001046225 scopus 로고
    • Practical issues in temporal difference learning
    • May
    • G. Tesauro, “Practical issues in temporal difference learning,” Machine Learning, Vol. 8, no. 3–4, pp. 257–77, May 1992.
    • (1992) Machine Learning , vol.8 , Issue.3-4 , pp. 257-277
    • Tesauro, G.1
  • 10
    • 69349090197 scopus 로고    scopus 로고
    • Learning deep architectures for AI
    • Jan
    • Y. Bengio, “Learning deep architectures for AI,” Foundations and trends® in Machine Learning, Vol. 2, no. 1, pp. 1–127, Jan. 2009.
    • (2009) Foundations and trends® in Machine Learning , vol.2 , Issue.1 , pp. 1-127
    • Bengio, Y.1
  • 11
    • 34547975052 scopus 로고    scopus 로고
    • Scaling learning algorithms towards AI
    • Sept., and
    • Y. Bengio, and Y. LeCun, “Scaling learning algorithms towards AI,” Large-Scale Kernel Machines, Vol. 34, pp. 1–41, Sept. 2007.
    • (2007) Large-Scale Kernel Machines , vol.34 , pp. 1-41
    • Bengio, Y.1    LeCun, Y.2
  • 12
    • 33745805403 scopus 로고    scopus 로고
    • A learning algorithm for deep belief nets
    • Jul., and Y. W. The
    • G. E. Hinton, S. Osindero, and Y. W. The, “A learning algorithm for deep belief nets,” Neural Computation, Vol. 18, no. 7, pp. 1527–54, Jul. 2006.
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2
  • 17
    • 0029652445 scopus 로고
    • The “wake-sleep” algorithm for unsupervised neural network
    • May, and
    • G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal, “The “wake-sleep” algorithm for unsupervised neural network,” Science, Vol. 268, no. 5214, pp. 1558–161, May 1995.
    • (1995) Science , vol.268 , Issue.5214 , pp. 1558-1161
    • Hinton, G.E.1    Dayan, P.2    Frey, B.J.3    Neal, R.M.4
  • 21
    • 0345368881 scopus 로고
    • Unsupervised learning of distributions on binary vectors using two layer networks
    • Moody J.E., Hanson S.J., Lippmann R.P., (eds), Denver, CO: Morgan Kaufmann, and, Eds
    • Y. Freund, and D. Haussler, “Unsupervised learning of distributions on binary vectors using two layer networks,” in Advances in Neural Information Processing Systems, Vol. 4, J. E. Moody, S. J. Hanson, and R.P. Lippmann, Eds. Denver, CO: Morgan Kaufmann, 1991, pp. 912–9.
    • (1991) Advances in Neural Information Processing Systems , vol.4 , pp. 912-919
    • Freund, Y.1    Haussler, D.2
  • 22
    • 0013344078 scopus 로고    scopus 로고
    • Training products of experts by minimizing contrastive divergence
    • Aug
    • G. E. Hinton, “Training products of experts by minimizing contrastive divergence,”Neural Computation, Vol. 14, no. 8, pp. 1771–800, Aug. 2002.
    • (2002) Neural Computation , vol.14 , Issue.8 , pp. 1771-1800
    • Hinton, G.E.1
  • 23
    • 33745780732 scopus 로고    scopus 로고
    • Exponential family harmoniums with an application to information retrieval
    • Saul L.K., Weiss Y., Bottou L., (eds), Cambridge, MA: MIT Press, and, Eds
    • M. Welling, M. Rosen-Zvi, and G. E. Hinton, “Exponential family harmoniums with an application to information retrieval,” in Advances in Neural Information Processing Systems, Vol. 17, L. K. Saul, Y. Weiss and L. Bottou, Eds. Cambridge, MA: MIT Press, 2004, pp. 1481–8.
    • (2004) Advances in Neural Information Processing Systems , vol.17 , pp. 1481-1488
    • Welling, M.1    Rosen-Zvi, M.2    Hinton, G.E.3
  • 25
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • May, and
    • G. E. Hinton, and R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, Vol. 313, no. 5786, pp. 504–7, May 2006.
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.2
  • 26
    • 56449095373 scopus 로고    scopus 로고
    • A unified architecture for natural language processing: Deep neural networks with multitask learning
    • Helsinki: 2008, and
    • R. Collobert, and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proceedings of International Conference on Machine learning, Helsinki, 2008, pp. 160–7.
    • Proceedings of International Conference on Machine learning , pp. 160-167
    • Collobert, R.1    Weston, J.2
  • 27
    • 84864069017 scopus 로고    scopus 로고
    • Efficient learning of sparse representations with an energy-based model
    • Schölkopf B., Platt J.C., Hoffman T., (eds), Cambridge: MIT Press, and, Eds
    • M. Ranzato, C. Poultney, S. Chopra, and Y. LeCun, “Efficient learning of sparse representations with an energy-based model,” in Advances in Neural Information Processing Systems, Vol. 19, B. Schölkopf, J. C. Platt and T. Hoffman, Eds. Cambridge: MIT Press, 2006, pp. 1137–44.
    • (2006) Advances in Neural Information Processing Systems , vol.19 , pp. 1137-1144
    • Ranzato, M.1    Poultney, C.2    Chopra, S.3    LeCun, Y.4
  • 31
    • 45749110924 scopus 로고    scopus 로고
    • Representational power of restricted Boltzmann machines and deep belief networks
    • Jun., and
    • N. Le Roux, and Y. Bengio, “Representational power of restricted Boltzmann machines and deep belief networks,” Neural Computation, Vol. 20, no. 6, pp. 1631–49, Jun. 2008.
    • (2008) Neural Computation , vol.20 , Issue.6 , pp. 1631-1649
    • Le Roux, N.1    Bengio, Y.2
  • 32
    • 78049383425 scopus 로고    scopus 로고
    • Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machines
    • Thessaloniki: 2010, and
    • A. Fischer, and C. Igel, “Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machines,” in Proceedings of the 20th International Conference on Artificial Neural Networks, Thessaloniki, 2010, pp. 208–17.
    • Proceedings of the 20th International Conference on Artificial Neural Networks , pp. 208-217
    • Fischer, A.1    Igel, C.2
  • 34
    • 35348818718 scopus 로고    scopus 로고
    • Learning multiple layers of representation
    • Oct
    • G. E. Hinton, “Learning multiple layers of representation,” Trends in Cognitive Sciences, Vol. 11, no. 10, pp. 428–34, Oct. 2007.
    • (2007) Trends in Cognitive Sciences , vol.11 , Issue.10 , pp. 428-434
    • Hinton, G.E.1
  • 35
    • 56449086223 scopus 로고    scopus 로고
    • Training restricted Boltzmann machines using approximations to the likelihood gradient
    • New York: 2008
    • T. Tieleman, “Training restricted Boltzmann machines using approximations to the likelihood gradient,” in Proceedings of the 25th International Conference on Machine Learning, New York, 2008, pp. 1064–71.
    • Proceedings of the 25th International Conference on Machine Learning , pp. 1064-1071
    • Tieleman, T.1
  • 37
    • 67651049775 scopus 로고    scopus 로고
    • Justifying and generalizing contrastive divergence
    • Jun., and
    • Y. Bengio, and O. Delalleau, “Justifying and generalizing contrastive divergence,” Neural Computation, Vol. 21, no. 6, pp. 1601–21, Jun. 2009.
    • (2009) Neural Computation , vol.21 , Issue.6 , pp. 1601-1621
    • Bengio, Y.1    Delalleau, O.2
  • 38
    • 78049383425 scopus 로고    scopus 로고
    • Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machines
    • Thessaloniki: 2010, and
    • A. Fischer, and C. Igel, “Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machines,” in Proceedings of the 20th International Conference on Artificial Neural Networks, Thessaloniki, 2010, pp. 208–17.
    • Proceedings of the 20th International Conference on Artificial Neural Networks , pp. 208-217
    • Fischer, A.1    Igel, C.2
  • 39
    • 28844454252 scopus 로고    scopus 로고
    • Parallel tempering: theory, applications, and new perspectives
    • –, Aug
    • D. J. Earl, and M. W. Deem, “Parallel tempering: theory, applications, and new perspectives,” Physical Chemistry Chemical Physics, Vol. 7, pp. 3910–6, Aug. 2005.
    • (2005) Physical Chemistry Chemical Physics , vol.7 , pp. 3910-3916
    • Earl, D.J.1    Deem, M.W.2
  • 41
    • 21444452127 scopus 로고    scopus 로고
    • Sampling from multimodal distributions using tempered transitions
    • Dec
    • R. M. Neal, “Sampling from multimodal distributions using tempered transitions,” Statistics and Computing, Vol. 6, no. 4, pp. 353–66, Dec. 1996.
    • (1996) Statistics and Computing , vol.6 , Issue.4 , pp. 353-366
    • Neal, R.M.1
  • 42
    • 0035600220 scopus 로고    scopus 로고
    • Extended ensemble monte carlo
    • Jun
    • Y. Iba, “Extended ensemble monte carlo,” International Journal of Modern Physics, Vol. 12, no. 5, pp. 623–56, Jun. 2001.
    • (2001) International Journal of Modern Physics , vol.12 , Issue.5 , pp. 623-656
    • Iba, Y.1
  • 43
    • 84901064273 scopus 로고    scopus 로고
    • Improving Mixing Rate with Tempered Transition for Learning Restricted Boltzmann Machines
    • Sept., and
    • J. Xu, H. Li, and S. Zhou, “Improving Mixing Rate with Tempered Transition for Learning Restricted Boltzmann Machines,” Neurocomputing, Vol. 139, pp. 328–35, Sept. 2014.
    • (2014) Neurocomputing , vol.139 , pp. 328-335
    • Xu, J.1    Li, H.2    Zhou, S.3
  • 44
    • 0001969496 scopus 로고
    • Learning sets of filters using back-propagation
    • Mar., and
    • D. C. Plaut, and G. E. Hinton, “Learning sets of filters using back-propagation,” Computer, Speech and Language, Vol. 2, no. 1, pp. 35–61, Mar. 1987.
    • (1987) Computer, Speech and Language , vol.2 , Issue.1 , pp. 35-61
    • Plaut, D.C.1    Hinton, G.E.2
  • 45
    • 0000362092 scopus 로고
    • Non-linear dimension reduction
    • Hanson S.J., Cowan J.D., Giles C.L., (eds), San Mateo, CA: Morgan Kaufmann, and, Eds
    • D. DeMers, and G. Cottrell, “Non-linear dimension reduction,” in Advances in Neural Information Processing Systems, Vol. 5, S. J. Hanson, J. D. Cowan and C. L. Giles, Eds. San Mateo, CA: Morgan Kaufmann, 1992, pp. 580–7.
    • (1992) Advances in Neural Information Processing Systems , vol.5 , pp. 580-587
    • DeMers, D.1    Cottrell, G.2
  • 46
    • 0000783715 scopus 로고
    • Replicator neural networks for universal optimal source coding
    • Sept
    • R. Hecht-Nielsen, “Replicator neural networks for universal optimal source coding,” Science, Vol. 269, no. 5232, pp. 1860–3, Sept. 1995.
    • (1995) Science , vol.269 , Issue.5232 , pp. 1860-1863
    • Hecht-Nielsen, R.1
  • 47
    • 0348139702 scopus 로고    scopus 로고
    • Dimension reduction by local principal component analysis
    • Oct., and
    • N. Kambhatla, and T. K. Leen, “Dimension reduction by local principal component analysis,” Neural Computation, Vol. 9, no. 7, pp. 1493–516, Oct. 1997.
    • (1997) Neural Computation , vol.9 , Issue.7 , pp. 1493-1516
    • Kambhatla, N.1    Leen, T.K.2
  • 49
    • 84887445321 scopus 로고    scopus 로고
    • A better way to pretrain deep Boltzmann machines
    • Pereira F., Burges C.J.C., Bottou L., Weinberger K.Q., (eds), Cambridge, MA: MIT Press, and, Eds
    • R. Salakhutdinov, and G. Hinton, “A better way to pretrain deep Boltzmann machines,” Advances in Neural Information Processing Systems, Vol. 25, F. Pereira, C. J. C. Burges, L. Bottou and K. Q. Weinberger, Eds. Cambridge, MA: MIT Press, 2012, pp. 1–9.
    • (2012) Advances in Neural Information Processing Systems , vol.25 , pp. 1-9
    • Salakhutdinov, R.1    Hinton, G.2
  • 50
    • 78651276374 scopus 로고    scopus 로고
    • Learning deep generative models
    • Graduate Department of Computer Science, Univ. Toronto, Toronto
    • R. Salakhutdinov, “Learning deep generative models,” Ph.D. Dissertation, Graduate Department of Computer Science, Univ. Toronto, Toronto, 2009.
    • (2009) Ph.D. Dissertation
    • Salakhutdinov, R.1
  • 51
    • 84876458566 scopus 로고    scopus 로고
    • Facial expression recognition using local transitional pattern on gabor filtered facial images
    • –, Jan
    • A. Tanveer, J. Taskeed, and C. Ui-Pil, “Facial expression recognition using local transitional pattern on gabor filtered facial images”, IETE Technical Review, Vol. 30, no. 1, pp. 47–52, Jan. 2013.
    • (2013) IETE Technical Review , vol.30 , Issue.1 , pp. 47-52
    • Tanveer, A.1    Taskeed, J.2    Ui-Pil, C.3
  • 52
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • Jul., and
    • G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, Vol. 18, no. 7, pp. 1527–54, Jul. 2006.
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.W.3
  • 54
    • 84866771105 scopus 로고    scopus 로고
    • An EM-based ensemble learning algorithm on piecewise surface regression problem
    • –, Aug
    • J. Luo, A. Brodsky, and Y. Li, “An EM-based ensemble learning algorithm on piecewise surface regression problem,” International Journal of Applied Mathematics and Statistics, Vol. 28, no. 4, pp. 59–74, Aug. 2012.
    • (2012) International Journal of Applied Mathematics and Statistics , vol.28 , Issue.4 , pp. 59-74
    • Luo, J.1    Brodsky, A.2    Li, Y.3
  • 55
    • 78149306047 scopus 로고    scopus 로고
    • 3-d object recognition with deep belief nets
    • Bengio Y., Schuurmans D., Lafferty J.D., Williams C.K.I., Culotta A., (eds), Cambridge, MA: MIT Press, and, Eds
    • V. Nair, and G.Hinton, “3-d object recognition with deep belief nets,” in Advances in Neural Information Processing Systems, Vol. 22, Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams and A. Culotta, Eds. Cambridge, MA: MIT Press, 2009, pp. 1339–47.
    • (2009) Advances in Neural Information Processing Systems , vol.22 , pp. 1339-1347
    • Nair, V.1    Hinton, G.2
  • 59
    • 84877724347 scopus 로고    scopus 로고
    • Multimodal learning with deep Boltzmann machines
    • Pereira F., Burges C.J.C., Bottou L., Weinberger K.Q., (eds), Montreal, Canada: NIPS, and, Eds
    • N. Srivastava, and R. Salakhutdinov, “Multimodal learning with deep Boltzmann machines,”in Advances in Neural Information Processing Systems, Vol. 25, F. Pereira, C. J. C. Burges, L. Bottou and K. Q. Weinberger, Eds. Montreal, Canada: NIPS, 2012, pp. 2222–30.
    • (2012) Advances in Neural Information Processing Systems , vol.25 , pp. 2222-2230
    • Srivastava, N.1    Salakhutdinov, R.2
  • 61
    • 84055212007 scopus 로고    scopus 로고
    • Sparse multilayer perceptron for phoneme recognition
    • Jan., and
    • G. Sivaram, and H. Hermansky, “Sparse multilayer perceptron for phoneme recognition,”IEEE Trans. Audio, Speech, & Language Processing, Vol. 20, no. 1, pp. 23–9, Jan. 2012.
    • (2012) IEEE Trans. Audio, Speech, & Language Processing , vol.20 , Issue.1 , pp. 23-29
    • Sivaram, G.1    Hermansky, H.2
  • 69
    • 84055222005 scopus 로고    scopus 로고
    • Context-dependent, pre-trained deep neural networks for large vocabulary speech recognition
    • Jan., and
    • G. Dahl, D. Yu, L. Deng, and A. Acero, “Context-dependent, pre-trained deep neural networks for large vocabulary speech recognition,” IEEE Trans. Audio, Speech, & Language Proc., Vol. 20, no. 1, pp. 30–42, Jan. 2012.
    • (2012) IEEE Trans. Audio, Speech, & Language Proc , vol.20 , Issue.1 , pp. 30-42
    • Dahl, G.1    Yu, D.2    Deng, L.3    Acero, A.4
  • 72
    • 79961219393 scopus 로고    scopus 로고
    • Discovering binary codes for documents by learning deep generative models
    • Jan., and
    • G. Hinton, and R. Salakhutdinov, “Discovering binary codes for documents by learning deep generative models,” Topics in Cognitive Science, Vol. 3, no. 1, pp. 74–91, Jan. 2011.
    • (2011) Topics in Cognitive Science , vol.3 , Issue.1 , pp. 74-91
    • Hinton, G.1    Salakhutdinov, R.2
  • 75
    • 84872431330 scopus 로고    scopus 로고
    • View of MapReduce: Programming model, methods, and its applications
    • Sept., and
    • W. Fang, W. Pan, and Z. Cui, “View of MapReduce: Programming model, methods, and its applications”, IETE Technical Review, Vol. 29, no. 5, pp. 380–7, Sept. 2012.
    • (2012) IETE Technical Review , vol.29 , Issue.5 , pp. 380-387
    • Fang, W.1    Pan, W.2    Cui, Z.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.