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Volumn 1, Issue January, 2014, Pages 855-863

On the computational efficiency of training neural networks

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; INFORMATION SCIENCE;

EID: 84937952927     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (480)

References (24)
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    • Improving deep neural networks for lvcsr using rectified linear units and dropout
    • G. Dahl, T. Sainath, and G. Hinton. Improving deep neural networks for lvcsr using rectified linear units and dropout. In ICASSP, 2013.
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    • Dahl, G.1    Sainath, T.2    Hinton, G.3
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    • Dalal, N.1    Triggs, B.2
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    • A. Daniely, N. Linial, and S. Shalev-Shwartz. From average case complexity to improper learning complexity. In FOCS, 2014.
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    • Daniely, A.1    Linial, N.2    Shalev-Shwartz, S.3
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    • Cryptographic hardness for learning intersections of halfspaces
    • A. Klivans and A. Sherstov. Cryptographic hardness for learning intersections of halfspaces. In FOCS, 2006.
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    • Klivans, A.1    Sherstov, A.2
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    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.