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Volumn 9489, Issue , 2015, Pages 46-54

Max-pooling dropout for regularization of convolutional neural networks

Author keywords

Convolutional neural network; Deep learning; Max pooling Dropout

Indexed keywords

INFORMATION SCIENCE; NEURAL NETWORKS; STOCHASTIC SYSTEMS;

EID: 84952767391     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-26532-2_6     Document Type: Conference Paper
Times cited : (122)

References (8)
  • 4
    • 85083954484 scopus 로고    scopus 로고
    • Stochastic pooling for regularization of deep convolutional neural networks
    • Zeiler, M.D., Fergus R.: Stochastic pooling for regularization of deep convolutional neural networks. In: ICLR (2013)
    • (2013) ICLR
    • Zeiler, M.D.1    Fergus, R.2
  • 6
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 7
    • 77956509090 scopus 로고    scopus 로고
    • Rectified linear units improve restricted Boltzmann machines
    • Vinod, N., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML (2010)
    • (2010) ICML
    • Vinod, N.1    Hinton, G.E.2
  • 8
    • 84896515095 scopus 로고    scopus 로고
    • Adaptive dropout for training deep neural networks
    • Ba, J.L., Frey, B.: Adaptive dropout for training deep neural networks. In: NIPS (2013)
    • (2013) NIPS
    • Ba, J.L.1    Frey, B.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.