메뉴 건너뛰기




Volumn 38, Issue 6, 2014, Pages 1078-1101

Where do features come from?

Author keywords

Backpropagation; Boltzmann machines; Contrastive divergence; Deep learning; Distributed representations; Learning features; Learning graphical models; Variational learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; COMPUTER SIMULATION; HUMAN; LEARNING;

EID: 84906221749     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1111/cogs.12049     Document Type: Article
Times cited : (89)

References (56)
  • 1
    • 0001862769 scopus 로고
    • An inequality and associated maximization technique in statistical estimation for probabilistic functions of markov processes
    • Baum, L. E. (1972). An inequality and associated maximization technique in statistical estimation for probabilistic functions of markov processes. Inequalities, 3, 1-8.
    • (1972) Inequalities , vol.3 , pp. 1-8
    • Baum, L.E.1
  • 2
  • 3
    • 81355133300 scopus 로고    scopus 로고
    • Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons
    • 10.1371/journal.pcbi.1002211.
    • Buesing, L., Bill, J., Nessler, B., & Maass, W. (2011). Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons. PLoS Computational Biology, 7. 10.1371/journal.pcbi.1002211.
    • (2011) PLoS Computational Biology , vol.7
    • Buesing, L.1    Bill, J.2    Nessler, B.3    Maass, W.4
  • 4
    • 0020966176 scopus 로고
    • The function of dream sleep
    • Crick, F., & Mitchison, G. (1986). The function of dream sleep. Nature, 304, 111-114.
    • (1986) Nature , vol.304 , pp. 111-114
    • Crick, F.1    Mitchison, G.2
  • 6
    • 0000362092 scopus 로고
    • Nonlinear dimensionality reduction
    • In C. Hanson & C. Giles (Eds.), San Mateo, CA: Morgan Kaufmann.
    • DeMers, D., & Cottrell, G. W. (1993). Nonlinear dimensionality reduction. In C. Hanson & C. Giles (Eds.), Advances in neural information processing systems, Vol. 5 (pp. 580-587). San Mateo, CA: Morgan Kaufmann.
    • (1993) Advances in neural information processing systems , vol.5 , pp. 580-587
    • DeMers, D.1    Cottrell, G.W.2
  • 8
    • 26444565569 scopus 로고
    • Finding structure in time
    • Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179-211.
    • (1990) Cognitive Science , vol.14 , Issue.2 , pp. 179-211
    • Elman, J.L.1
  • 11
    • 0000783715 scopus 로고
    • Replicator neural networks for universal optimal source coding
    • Hecht-Nielsen, R. (1995). Replicator neural networks for universal optimal source coding. Science, 269, 1860-1863.
    • (1995) Science , vol.269 , pp. 1860-1863
    • Hecht-Nielsen, R.1
  • 12
    • 0022823858 scopus 로고
    • Probabilistic interpretations for mycin's certainty factors
    • In L. Kanal & J. Lemmer (Eds.), New York: North-Holland.
    • Heckerman, D. (1986). Probabilistic interpretations for mycin's certainty factors. In L. Kanal & J. Lemmer (Eds.), Uncertainty in artificial intelligence (pp. 167-196). New York: North-Holland.
    • (1986) Uncertainty in artificial intelligence , pp. 167-196
    • Heckerman, D.1
  • 13
    • 0024732792 scopus 로고
    • Connectionist learning procedures
    • Hinton, G. E. (1989). Connectionist learning procedures. Artificial Intelligence, 40, 185-234.
    • (1989) Artificial Intelligence , vol.40 , pp. 185-234
    • Hinton, G.E.1
  • 14
    • 0013344078 scopus 로고    scopus 로고
    • Training products of experts by minimizing contrastive divergence
    • Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8), 1711-1800.
    • (2002) Neural Computation , vol.14 , Issue.8 , pp. 1711-1800
    • Hinton, G.E.1
  • 16
    • 0029652445 scopus 로고
    • The wake-sleep algorithm for self-organizing neural networks
    • Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. (1995). The wake-sleep algorithm for self-organizing neural networks. Science, 268, 1158-1161.
    • (1995) Science , vol.268 , pp. 1158-1161
    • Hinton, G.E.1    Dayan, P.2    Frey, B.J.3    Neal, R.4
  • 18
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554.
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.W.3
  • 19
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313, 504-507.
    • (2006) Science , vol.313 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 20
    • 0000999440 scopus 로고
    • Learning and relearning in Boltzmann machines
    • In D. E. Rumelhart & J. L. McClelland (Eds.), Cambridge, MA: MIT Press.
    • Hinton, G. E., & Sejnowski, T. J. (1986). Learning and relearning in Boltzmann machines. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Volume 1: Foundations (pp. 282-317). Cambridge, MA: MIT Press.
    • (1986) Parallel distributed processing: Volume 1: Foundations , pp. 282-317
    • Hinton, G.E.1    Sejnowski, T.J.2
  • 23
    • 0017478996 scopus 로고
    • Understanding image intensities
    • Horn, B. K. P. (1977). Understanding image intensities. Artificial Intelligence, 8, 201-231.
    • (1977) Artificial Intelligence , vol.8 , pp. 201-231
    • Horn, B.K.P.1
  • 24
    • 0000935895 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • In M. I. Jordan (Ed.), Cambridge, MA: MIT Press.
    • Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. In M. I. Jordan (Ed.), Learning in graphical models (pp. 105-161). Cambridge, MA: MIT Press.
    • (1999) Learning in graphical models , pp. 105-161
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.S.3    Saul, L.K.4
  • 25
    • 0001006209 scopus 로고
    • Local computations with probabilities on graphical structures and their application to expert systems
    • Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society B, 50, 157-224.
    • (1988) Journal of the Royal Statistical Society B , vol.50 , pp. 157-224
    • Lauritzen, S.L.1    Spiegelhalter, D.J.2
  • 26
    • 0000134812 scopus 로고
    • Une procédure d'apprentissage pour réseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks)
    • In F. Fogelman (Ed.), Paris, France.
    • LeCun, Y. (1985). Une procédure d'apprentissage pour réseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks). In F. Fogelman (Ed.), Proceedings of cognitiva, (pp. 599-604). Paris, France.
    • (1985) Proceedings of cognitiva , pp. 599-604
    • LeCun, Y.1
  • 27
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
    • (1998) Proceedings of the IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 28
    • 71149119164 scopus 로고    scopus 로고
    • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    • In L. Bottou & M. Littman (Eds.), Montreal: ACM.
    • Lee, H., Grosse, R., Ranganath, R., & Ng, A. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In L. Bottou & M. Littman (Eds.), Proceedings of the 26th international conference on machine learning (pp. 609-616). Montreal: ACM.
    • (2009) Proceedings of the 26th international conference on machine learning , pp. 609-616
    • Lee, H.1    Grosse, R.2    Ranganath, R.3    Ng, A.4
  • 30
    • 0031012615 scopus 로고    scopus 로고
    • Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs
    • Markram, H., Joachim, L., Frotscher, M., & Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275, 213-215.
    • (1997) Science , vol.275 , pp. 213-215
    • Markram, H.1    Joachim, L.2    Frotscher, M.3    Sakmann, B.4
  • 31
    • 77956541496 scopus 로고    scopus 로고
    • Deep learning via Hessian-free optimization
    • In J. Furnkranz & T. Joachims (Eds.), Haifa, Israel: Omnipress.
    • Martens, J. (2010). Deep learning via Hessian-free optimization. In J. Furnkranz & T. Joachims (Eds.), Proceedings of the 27th international conference on machine learning (ICML) (pp. 735-742). Haifa, Israel: Omnipress.
    • (2010) Proceedings of the 27th international conference on machine learning (ICML) , pp. 735-742
    • Martens, J.1
  • 32
    • 58149409989 scopus 로고
    • An interactive activation model of context effects in letter perception: Part 1. An account of basic findings
    • McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review, 88, 375-407.
    • (1981) Psychological Review , vol.88 , pp. 375-407
    • McClelland, J.L.1    Rumelhart, D.E.2
  • 33
    • 77956509090 scopus 로고    scopus 로고
    • Rectified linear units improve restricted Boltzmann machines
    • In J. Furnkranz & T. Joachims (Eds.), Haifa, Israel: Omnipress.
    • Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. In J. Furnkranz & T. Joachims (Eds.), Proceedings of the 27th international conference on machine learning (pp. 807-814). Haifa, Israel: Omnipress.
    • (2010) Proceedings of the 27th international conference on machine learning , pp. 807-814
    • Nair, V.1    Hinton, G.E.2
  • 34
    • 44049116681 scopus 로고
    • Connectionist learning of belief networks
    • Neal, R. M. (1992). Connectionist learning of belief networks. Artificial Intelligence, 56(1), 71-113.
    • (1992) Artificial Intelligence , vol.56 , Issue.1 , pp. 71-113
    • Neal, R.M.1
  • 35
    • 84906268425 scopus 로고
    • Bayesian learning for neural networks. PhD thesis, Department of Computer Science, University of Toronto.
    • Neal, R. M. (1994). Bayesian learning for neural networks. PhD thesis, Department of Computer Science, University of Toronto.
    • (1994)
    • Neal, R.M.1
  • 36
    • 0002788893 scopus 로고    scopus 로고
    • A new view of the EM algorithm that justifies incremental, sparse and other variants
    • In M. I. Jordan (Ed.), Dordrecht, The Netherlands: Kluwer Academic Publishers.
    • Neal, R. M., & Hinton, G. E. (1998). A new view of the EM algorithm that justifies incremental, sparse and other variants. In M. I. Jordan (Ed.), Learning in graphical models (pp. 355-368). Dordrecht, The Netherlands: Kluwer Academic Publishers.
    • (1998) Learning in graphical models , pp. 355-368
    • Neal, R.M.1    Hinton, G.E.2
  • 37
    • 0027261536 scopus 로고
    • Phase relationship between hippocampal place units and the EEG theta rhythm
    • O'Keefe, J., & Recce, M. L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus, 3, 317-330.
    • (1993) Hippocampus , vol.3 , pp. 317-330
    • O'Keefe, J.1    Recce, M.L.2
  • 39
    • 70049094447 scopus 로고    scopus 로고
    • Sparse feature learning for deep belief networks
    • In B. Scholkopf, J. Platt, & T. Huffman (Eds.), San Mateo, CA: Morgan Kaufmann.
    • Ranzato, M., Boureau, Y., & LeCun, Y. (2007). Sparse feature learning for deep belief networks. In B. Scholkopf, J. Platt, & T. Huffman (Eds.), Advances in neural information processing systems, Vol. 20 (pp. 1185-1192). San Mateo, CA: Morgan Kaufmann.
    • (2007) Advances in neural information processing systems , vol.20 , pp. 1185-1192
    • Ranzato, M.1    Boureau, Y.2    LeCun, Y.3
  • 41
    • 85161966240 scopus 로고    scopus 로고
    • Hallucinations in Charles Bonnet syndrome induced by homeostasis: A deep Boltzmann machine model
    • In J. Lufferty & C. Williams (Eds.), San Mateo, CA: Morgan Kaufmann.
    • Reichert, D. P., Series, P., & Storkey, A. J. (2010). Hallucinations in Charles Bonnet syndrome induced by homeostasis: A deep Boltzmann machine model. In J. Lufferty & C. Williams (Eds.), Advances in neural information processing systems, Vol. 23 (pp. 2020-2028). San Mateo, CA: Morgan Kaufmann.
    • (2010) Advances in neural information processing systems , vol.23 , pp. 2020-2028
    • Reichert, D.P.1    Series, P.2    Storkey, A.J.3
  • 44
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986b). Learning representations by back-propagating errors. Nature, 323, 533-536.
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 45
    • 84874125782 scopus 로고    scopus 로고
    • An efficient learning procedure for deep Boltzmann machines
    • Salakhutdinov, R. R., & Hinton, G. E. (2012). An efficient learning procedure for deep Boltzmann machines. Neural Computation, 24, 1967-2006.
    • (2012) Neural Computation , vol.24 , pp. 1967-2006
    • Salakhutdinov, R.R.1    Hinton, G.E.2
  • 47
    • 0000329993 scopus 로고
    • Information processing in dynamical systems: Foundations of harmony theory
    • In D. E. Rumelhart & J. L. McClelland (Eds.), Cambridge, MA: MIT Press.
    • Smolensky, P. (1986). Information processing in dynamical systems: Foundations of harmony theory. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Volume 1: Foundations (pp. 194-281). Cambridge, MA: MIT Press.
    • (1986) Parallel distributed processing: Volume 1: Foundations , pp. 194-281
    • Smolensky, P.1
  • 48
    • 79955836081 scopus 로고    scopus 로고
    • Two distributed-state models for generating high-dimensional time series
    • Taylor, G. W., Hinton, G. E., & Roweis, S. (2011). Two distributed-state models for generating high-dimensional time series. Journal of Machine Learning Research, 12, 1025-1068.
    • (2011) Journal of Machine Learning Research , vol.12 , pp. 1025-1068
    • Taylor, G.W.1    Hinton, G.E.2    Roweis, S.3
  • 49
    • 33746260413 scopus 로고    scopus 로고
    • Theory-based Bayesian models of inductive learning and reasoning
    • Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10, 309-318.
    • (2006) Trends in Cognitive Sciences , vol.10 , pp. 309-318
    • Tenenbaum, J.B.1    Griffiths, T.L.2    Kemp, C.3
  • 50
    • 56449086223 scopus 로고    scopus 로고
    • Training restricted Boltzmann machines using approximations to the likelihood gradient
    • In A. McCallum & S. Roweis (Eds.), New York: ACM.
    • Tieleman, T. (2008). Training restricted Boltzmann machines using approximations to the likelihood gradient. In A. McCallum & S. Roweis (Eds.), Proceedings of the 25th international conference on machine learning (pp. 1064-1071). New York: ACM.
    • (2008) Proceedings of the 25th international conference on machine learning , pp. 1064-1071
    • Tieleman, T.1
  • 51
    • 71149084943 scopus 로고    scopus 로고
    • Using fast weights to improve persistent contrastive divergence
    • In L. Bottou M. Littman (Eds.), New York: ACM.
    • Tieleman, T., & Hinton, G. E. (2009). Using fast weights to improve persistent contrastive divergence. In L. Bottou M. Littman (Eds.), Proceedings of the 26th international conference on machine learning (pp. 1033-1040). New York: ACM.
    • (2009) Proceedings of the 26th international conference on machine learning , pp. 1033-1040
    • Tieleman, T.1    Hinton, G.E.2
  • 52
    • 79551480483 scopus 로고    scopus 로고
    • Stacked denoising auto-encoders: Learning useful representations in a deep network with a local denoising criterion
    • Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P.-A. (2010). Stacked denoising auto-encoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11, 3371-3408.
    • (2010) Journal of Machine Learning Research , vol.11 , pp. 3371-3408
    • Vincent, P.1    Larochelle, H.2    Lajoie, I.3    Bengio, Y.4    Manzagol, P.-A.5
  • 54
    • 84899000641 scopus 로고    scopus 로고
    • Exponential family harmoniums with an application to information retrieval
    • In L. Saul, Y. Weiss, & L. Bottou (Eds.), Cambridge, MA: MIT Press.
    • Welling, M., Rosen-Zvi, M., & Hinton, G. E. (2005). Exponential family harmoniums with an application to information retrieval. In L. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing systems (pp. 1481-1488). Cambridge, MA: MIT Press.
    • (2005) Advances in neural information processing systems , pp. 1481-1488
    • Welling, M.1    Rosen-Zvi, M.2    Hinton, G.E.3
  • 55
    • 84906229104 scopus 로고
    • Beyond regression: New tools for prediction and analysis in the behavioral sciences. PhD thesis, Harvard University.
    • Werbos, P. J. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences. PhD thesis, Harvard University.
    • (1974)
    • Werbos, P.J.1
  • 56
    • 0033362601 scopus 로고    scopus 로고
    • Evolving artificial neural Networks
    • Yao, X. (1999). Evolving artificial neural Networks. Proceedings of the IEEE, 87, 1423-1447.
    • (1999) Proceedings of the IEEE , vol.87 , pp. 1423-1447
    • Yao, X.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.