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Volumn 2015-January, Issue , 2015, Pages 3460-3468

Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BENCHMARKING; EXTRAPOLATION; LEARNING SYSTEMS; NETWORK ARCHITECTURE; NEURAL NETWORKS; OBJECT RECOGNITION; OPTIMIZATION; STOCHASTIC SYSTEMS;

EID: 84949921865     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (602)

References (36)
  • 1
    • 0034241361 scopus 로고    scopus 로고
    • Gradient-based optimization of hyperparameters
    • Y. Bengio. Gradient-based optimization of hyperparameters. Neural Computation, 12(8):1889-1900, 2000.
    • (2000) Neural Computation , vol.12 , Issue.8 , pp. 1889-1900
    • Bengio, Y.1
  • 2
    • 84857855190 scopus 로고    scopus 로고
    • Random search for hyper-parameter optimization
    • J. Bergstra and Y. Bengio. Random search for hyper-parameter optimization. JMLR, 13(1):281-305, 2012.
    • (2012) JMLR , vol.13 , Issue.1 , pp. 281-305
    • Bergstra, J.1    Bengio, Y.2
  • 4
    • 84897558007 scopus 로고    scopus 로고
    • Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
    • J. Bergstra, D. Yamins, and D.D. Cox. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In Proc. of ICML, pages 115-123, 2013.
    • (2013) Proc. of ICML , pp. 115-123
    • Bergstra, J.1    Yamins, D.2    Cox, D.D.3
  • 5
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • L. Breiman. Random forests. Machine learning, 45(1):5-32, 2001.
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 6
    • 84869826137 scopus 로고    scopus 로고
    • A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
    • E. Brochu, V. M. Cora, and N. de Freitas. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. CoRR, abs/1012.2599, 2010.
    • (2010) CoRR, Abs/1012.2599
    • Brochu, E.1    Cora, V.M.2    De Freitas, N.3
  • 7
    • 84862283411 scopus 로고    scopus 로고
    • An analysis of single-layer networks in unsupervised feature learning
    • A. Coates, A. Y. Ng, and H. Lee. An analysis of single-layer networks in unsupervised feature learning. In Proc. of AISTATS, pages 215-223, 2011.
    • (2011) Proc. of AISTATS , pp. 215-223
    • Coates, A.1    Ng, A.Y.2    Lee, H.3
  • 8
    • 84890527827 scopus 로고    scopus 로고
    • Improving deep neural networks for lvcsr using rectified linear units and dropout
    • IEEE
    • G. Dahl, T. Sainath, and G. Hinton. Improving deep neural networks for lvcsr using rectified linear units and dropout. In Proc. of ICASSP, pages 8609-8613. IEEE, 2013.
    • (2013) Proc. of ICASSP , pp. 8609-8613
    • Dahl, G.1    Sainath, T.2    Hinton, G.3
  • 9
    • 84890526837 scopus 로고    scopus 로고
    • New types of deep neural network learning for speech recognition and related applications: An overview
    • L. Deng, G. Hinton, and B. Kingsbury. New types of deep neural network learning for speech recognition and related applications: An overview. In Proc. of ICASSP, 2013.
    • (2013) Proc. of ICASSP
    • Deng, L.1    Hinton, G.2    Kingsbury, B.3
  • 13
    • 0012330992 scopus 로고    scopus 로고
    • Modeling decision tree performance with the power law
    • L. Frey and D. Fisher. Modeling decision tree performance with the power law. In Proc. of AISTATS, 1999.
    • (1999) Proc. of AISTATS
    • Frey, L.1    Fisher, D.2
  • 14
    • 84862277874 scopus 로고    scopus 로고
    • Understanding the difficulty of training deep feedforward neural networks
    • X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proc. of AISTATS, pages 249-256, 2010.
    • (2010) Proc. of AISTATS , pp. 249-256
    • Glorot, X.1    Bengio, Y.2
  • 16
    • 84974711038 scopus 로고    scopus 로고
    • Modelling classification performance for large data sets
    • Springer
    • B. Gu, F. Hu, and H. Liu. Modelling classification performance for large data sets. In Proc. of WAIM, pages 317-328. Springer, 2001.
    • (2001) Proc. of WAIM , pp. 317-328
    • Gu, B.1    Hu, F.2    Liu, H.3
  • 17
    • 84868554032 scopus 로고    scopus 로고
    • Sequential model-based optimization for general algorithm configuration
    • Springer
    • F. Hutter, H. Hoos, and K. Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In Proc. of LION, pages 507-523. Springer, 2011.
    • (2011) Proc. of LION , pp. 507-523
    • Hutter, F.1    Hoos, H.2    Leyton-Brown, K.3
  • 18
    • 84887848457 scopus 로고    scopus 로고
    • Algorithm runtime prediction: Methods and evaluation
    • F. Hutter, L. Xu, H. H. Hoos, and K. Leyton-Brown. Algorithm runtime prediction: Methods and evaluation. AIJ, 206(0):79-111, 2014.
    • (2014) AIJ , vol.206 , pp. 79-111
    • Hutter, F.1    Xu, L.2    Hoos, H.H.3    Leyton-Brown, K.4
  • 21
    • 0000561424 scopus 로고    scopus 로고
    • Efficient global optimization of expensive black-box functions
    • D. Jones, M. Schonlau, and W. Welch. Efficient global optimization of expensive black-box functions. Journal of Global optimization, 13(4):455-492, 1998.
    • (1998) Journal of Global Optimization , vol.13 , Issue.4 , pp. 455-492
    • Jones, D.1    Schonlau, M.2    Welch, W.3
  • 23
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In Proc. of NIPS, pages 1097-1105, 2012.
    • (2012) Proc. of NIPS , pp. 1097-1105
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.3
  • 28
    • 0001923944 scopus 로고
    • Hoeffding races: Accelerating model selection search for classification and function approximation
    • O. Maron and A. Moore. Hoeffding races: Accelerating model selection search for classification and function approximation. In Proc. of NIPS, pages 59-66, 1994.
    • (1994) Proc. of NIPS , pp. 59-66
    • Maron, O.1    Moore, A.2
  • 31
    • 84869201485 scopus 로고    scopus 로고
    • Practical Bayesian optimization of machine learning algorithms
    • J. Snoek, H. Larochelle, and R.P. Adams. Practical Bayesian optimization of machine learning algorithms. In Proc. of NIPS, pages 2951-2959, 2012.
    • (2012) Proc. of NIPS , pp. 2951-2959
    • Snoek, J.1    Larochelle, H.2    Adams, R.P.3
  • 36
    • 34547435898 scopus 로고    scopus 로고
    • On early stopping in gradient descent learning
    • Y. Yao, L. Rosasco, and A. Caponnetto. On early stopping in gradient descent learning. Constructive Approximation, 26(2):289-315, 2007.
    • (2007) Constructive Approximation , vol.26 , Issue.2 , pp. 289-315
    • Yao, Y.1    Rosasco, L.2    Caponnetto, A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.