메뉴 건너뛰기




Volumn , Issue , 2016, Pages 2270-2278

Learning the number of neurons in deep networks

Author keywords

[No Author keywords available]

Indexed keywords

COMPACT ARCHITECTURE; COMPUTATION COSTS; GROUP SPARSITIES; NETWORK ACCURACY; NUMBER OF LAYERS; OVER-COMPLETE; REDUNDANT PARAMETERS; SINGLE NEURON;

EID: 85018888114     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (460)

References (36)
  • 1
    • 85018934590 scopus 로고    scopus 로고
    • Decomposeme: Simplifying convnets for end-to-end learning
    • J.M. Alvarez and L. Petersson. Decomposeme: Simplifying convnets for end-to-end learning. CoRR, abs/1606.05426, 2016.
    • (2016) CoRR
    • Alvarez, J.M.1    Petersson, L.2
  • 2
    • 84945797434 scopus 로고
    • Dynamic node creation in backpropagation networks
    • T. Ash. Dynamic node creation in backpropagation networks. Connection Science, 1(4):365-375, 1989.
    • (1989) Connection Science , vol.1 , Issue.4 , pp. 365-375
    • Ash, T.1
  • 3
    • 0000593070 scopus 로고    scopus 로고
    • For valid generalization the size of the weights is more important than the size of the network
    • P. L. Bartlett. For valid generalization the size of the weights is more important than the size of the network. In NIPS, 1996.
    • (1996) NIPS
    • Bartlett, P.L.1
  • 4
    • 0026953321 scopus 로고
    • Enhanced training algorithms, and integrated training/architecture selection for multilayer perceptron networks
    • Nov
    • M. G. Bello. Enhanced training algorithms, and integrated training/architecture selection for multilayer perceptron networks. IEEE Transactions on Neural Networks, 3(6):864-875, Nov 1992.
    • (1992) IEEE Transactions on Neural Networks , vol.3 , Issue.6 , pp. 864-875
    • Bello, M.G.1
  • 5
    • 84973890879 scopus 로고    scopus 로고
    • An exploration of parameter redundancy in deep networks with circulant projections
    • Yu Cheng, Felix X. Yu, Rogério Schmidt Feris, Sanjiv Kumar, Alok N. Choudhary, and Shih-Fu Chang. An exploration of parameter redundancy in deep networks with circulant projections. In ICCV, 2015.
    • (2015) ICCV
    • Cheng, Y.1    Yu, F.X.2    Feris, R.S.3    Kumar, S.4    Choudhary, A.N.5    Chang, S.-F.6
  • 6
    • 84986246906 scopus 로고    scopus 로고
    • Memory bounded deep convolutional networks
    • M. D. Collins and P. Kohli. Memory Bounded Deep Convolutional Networks. CoRR, abs/1412.1442, 2014.
    • (2014) CoRR
    • Collins, M.D.1    Kohli, P.2
  • 9
    • 84937896655 scopus 로고    scopus 로고
    • Exploiting linear structure within convolutional networks for efficient evaluation
    • E. L Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus. Exploiting linear structure within convolutional networks for efficient evaluation. In NIPS. 2014.
    • (2014) NIPS
    • Denton, E.L.1    Zaremba, W.2    Bruna, J.3    LeCun, Y.4    Fergus, R.5
  • 12
    • 84943270068 scopus 로고
    • Optimal brain surgeon and general network pruning
    • B. Hassibi, D. G. Stork, and G. J. Wolff. Optimal brain surgeon and general network pruning. In ICNN, 1993.
    • (1993) ICNN
    • Hassibi, B.1    Stork, D.G.2    Wolff, G.J.3
  • 16
    • 85062833929 scopus 로고    scopus 로고
    • Speeding up convolutional neural networks with low rank expansions
    • M. Jaderberg, A. Vedaldi, and A. Zisserman. Speeding up convolutional neural networks with low rank expansions. In BMVC, 2014b.
    • (2014) BMVC
    • Jaderberg, M.1    Vedaldi, A.2    Zisserman, A.3
  • 17
    • 0000974760 scopus 로고
    • Generalizing smoothness constraints from discrete samples
    • June
    • C. Ji, R. R. Snapp, and D. Psaltis. Generalizing smoothness constraints from discrete samples. Neural Computation, 2(2):188-197, June 1990. ISSN 0899-7667.
    • (1990) Neural Computation , vol.2 , Issue.2 , pp. 188-197
    • Ji, C.1    Snapp, R.R.2    Psaltis, D.3
  • 18
    • 0000029122 scopus 로고
    • A simple weight decay can improve generalization
    • A. Krogh and J. A. Hertz. A simple weight decay can improve generalization. In NIPS, 1992.
    • (1992) NIPS
    • Krogh, A.1    Hertz, J.A.2
  • 21
    • 84930634427 scopus 로고    scopus 로고
    • On the number of linear regions of deep neural networks
    • G. F Montufar, R. Pascanu, K. Cho, and Y. Bengio. On the number of linear regions of deep neural networks. In NIPS. 2014.
    • (2014) NIPS
    • Montufar, G.F.1    Pascanu, R.2    Cho, K.3    Bengio, Y.4
  • 22
    • 0000900876 scopus 로고
    • Skeletonization: A technique for trimming the fat from a network via relevance assessment
    • M. Mozer and P. Smolensky. Skeletonization: A technique for trimming the fat from a network via relevance assessment. In NIPS, 1988.
    • (1988) NIPS
    • Mozer, M.1    Smolensky, P.2
  • 23
    • 85018936270 scopus 로고    scopus 로고
    • Auto-sizing neural networks: With applications to n-gram language models
    • K. Murray and D. Chiang. Auto-sizing neural networks: With applications to n-gram language models. CoRR, abs/1508.05051, 2015.
    • (2015) CoRR
    • Murray, K.1    Chiang, D.2
  • 24
    • 84884129062 scopus 로고    scopus 로고
    • Proximal algorithms
    • January
    • N. Parikh and S. Boyd. Proximal algorithms. Found. Trends Optim., 1(3):127-239, January 2014.
    • (2014) Found. Trends Optim. , vol.1 , Issue.3 , pp. 127-239
    • Parikh, N.1    Boyd, S.2
  • 25
    • 0027662338 scopus 로고
    • Pruning algorithms - A survey
    • Sep
    • R. Reed. Pruning algorithms - a survey. IEEE Transactions on Neural Networks, 4(5):740-747, Sep 1993.
    • (1993) IEEE Transactions on Neural Networks , vol.4 , Issue.5 , pp. 740-747
    • Reed, R.1
  • 28
    • 84920691110 scopus 로고    scopus 로고
    • Learning block group sparse representation combined with convolutional neural networks for rgb-d object recognition
    • J. Wang X. Huang, X. Zhang S. Tu, Y. Xue. Learning block group sparse representation combined with convolutional neural networks for rgb-d object recognition. Journal of Fiber Bioengineering and Informatics, 7(4):603, 2014.
    • (2014) Journal of Fiber Bioengineering and Informatics , vol.7 , Issue.4 , pp. 603
    • Wang, J.1    Huang, X.2    Zhang, X.3    Tu, S.4    Xue, Y.5
  • 30
    • 84933585162 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition
    • K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556, 2014.
    • (2014) CoRR
    • Simonyan, K.1    Zisserman, A.2
  • 33
    • 0000539096 scopus 로고
    • Generalization by weight-elimination with application to forecasting
    • A. S. Weigend, D. Rumelhart, and B. A. Huberman. Generalization by weight-elimination with application to forecasting. In NIPS, 1991.
    • (1991) NIPS
    • Weigend, A.S.1    Rumelhart, D.2    Huberman, B.A.3
  • 34
    • 33645035051 scopus 로고    scopus 로고
    • Model selection and estimation in regression with grouped variables
    • M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B, 68(1):49-67, 2007.
    • (2007) Journal of the Royal Statistical Society, Series B , vol.68 , Issue.1 , pp. 49-67
    • Yuan, M.1    Lin, Y.2
  • 36
    • 85018924264 scopus 로고    scopus 로고
    • Less is more: Towards compact CNNs
    • H. Zhou, J. M. Alvarez, and F. Porikli. Less is more: Towards compact CNNs. In ECCV, 2016.
    • (2016) ECCV
    • Zhou, H.1    Alvarez, J.M.2    Porikli, F.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.