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Volumn , Issue , 2016, Pages

Policy distillation

Author keywords

[No Author keywords available]

Indexed keywords

DEEP LEARNING; DISTILLATION; MACHINE LEARNING; VISION;

EID: 85083952240     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (262)

References (27)
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    • July
    • Rich Caruana. Multitask learning. Mach. Learn., 28(1):41–75, July 1997.
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    • Caruana, R.1
  • 7
    • 0022681148 scopus 로고
    • How not to lie with statistics: The correct way to summarize benchmark results
    • March
    • Philip J. Fleming and John J. Wallace. How not to lie with statistics: The correct way to summarize benchmark results. Commun. ACM, 29(3):218–221, March 1986.
    • (1986) Commun. ACM , vol.29 , Issue.3 , pp. 218-221
    • Fleming, P.J.1    Wallace, J.J.2
  • 11
    • 33750293964 scopus 로고    scopus 로고
    • Bandit based monte-carlo planning
    • Springer
    • Levente Kocsis and Csaba Szepesvári. Bandit based monte-carlo planning. In Machine Learning: ECML 2006, pages 282–293. Springer, 2006.
    • (2006) Machine Learning: ECML 2006 , pp. 282-293
    • Kocsis, L.1    Szepesvári, C.2
  • 13
    • 84910035297 scopus 로고    scopus 로고
    • Learning small-size dnn with output-distribution-based criteria
    • Jinyu Li, Rui Zhao, Jui-Ting Huang, and Yifan Gong. Learning small-size dnn with output-distribution-based criteria. In Proc. Interspeech, 2014.
    • (2014) Proc. Interspeech
    • Li, J.1    Zhao, R.2    Huang, J.-T.3    Gong, Y.4
  • 15
    • 65249159583 scopus 로고    scopus 로고
    • Improving supervised learning by adapting the problem to the learner
    • Joshua Menke and Tony Martinez. Improving supervised learning by adapting the problem to the learner. International Journal of Neural Systems, 19(01):1–9, 2009.
    • (2009) International Journal of Neural Systems , vol.19 , Issue.1 , pp. 1-9
    • Menke, J.1    Martinez, T.2
  • 24
    • 84893343292 scopus 로고    scopus 로고
    • Lecture 6.5—rMSprop: Divide the gradient by a running average of its recent magnitude
    • T. Tieleman and G. Hinton. Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 2012.
    • (2012) COURSERA: Neural Networks for Machine Learning
    • Tieleman, T.1    Hinton, G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.