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Volumn 9284, Issue , 2015, Pages 498-515

Difference target propagation

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FUNCTIONS; LEARNING SYSTEMS; STOCHASTIC SYSTEMS;

EID: 84984663965     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-23528-8_31     Document Type: Conference Paper
Times cited : (319)

References (23)
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    • Bengio, Y.: Estimating or propagating gradients through stochastic neurons. Tech. Rep. Universite de Montreal (2013). arXiv:1305.2982
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  • 4
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    • How auto-encoders could provide credit assignment in deep networks via target propagation
    • arXiv:1407.7906
    • Bengio, Y.: How auto-encoders could provide credit assignment in deep networks via target propagation. Tech. rep. (2014). arXiv:1407.7906
    • (2014) Tech. Rep
    • Bengio, Y.1
  • 6
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    • Deep generative stochastic networks trainable by backprop
    • Bengio, Y., Thibodeau-Laufer, E., Yosinski, J.: Deep generative stochastic networks trainable by backprop. In: ICML 2014 (2014)
    • (2014) ICML 2014
    • Bengio, Y.1    Thibodeau-Laufer, E.2    Yosinski, J.3
  • 7
    • 84857855190 scopus 로고    scopus 로고
    • Random search for hyper-parameter optimization
    • Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Machine Learning Res. 13, 281–305 (2012)
    • (2012) J. Machine Learning Res , vol.13 , pp. 281-305
    • Bergstra, J.1    Bengio, Y.2
  • 9
    • 84955486808 scopus 로고    scopus 로고
    • Distributed optimization of deeply nested systems
    • Carreira-Perpinan, M., Wang, W.: Distributed optimization of deeply nested systems. In: AISTATS 2014, JMLR W&CP, vol. 33, pp. 10–19 (2014)
    • (2014) AISTATS 2014, JMLR W&Cp , vol.33 , pp. 10-19
    • Carreira-Perpinan, M.1    Wang, W.2
  • 14
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: NIPS 2012 (2012)
    • (2012) NIPS 2012
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.3
  • 16
    • 0002824144 scopus 로고
    • Learning processes in an asymmetric threshold network
    • Fogelman- Soulié, F., Bienenstock, E., Weisbuch, G. (eds.), Springer-Verlag, Les Houches
    • LeCun, Y.: Learning processes in an asymmetric threshold network. In: Fogelman- Soulié, F., Bienenstock, E., Weisbuch, G. (eds.) Disordered Systems and Biological Organization, pp. 233–240. Springer-Verlag, Les Houches (1986)
    • (1986) Disordered Systems and Biological Organization , pp. 233-240
    • Lecun, Y.1
  • 19
    • 84910597353 scopus 로고    scopus 로고
    • Sequence to sequence learning with neural networks
    • arXiv:1409.3215
    • Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Tech. rep. (2014). arXiv:1409.3215
    • (2014) Tech. Rep
    • Sutskever, I.1    Vinyals, O.2    Le, Q.V.3
  • 22
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    • Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
    • Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning 4 (2012)
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    • Tieleman, T.1    Hinton, G.2
  • 23
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    • Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
    • Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Machine Learning Res. 11 (2010)
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.