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Volumn , Issue , 2011, Pages

Sparse filtering

Author keywords

[No Author keywords available]

Indexed keywords

COST FUNCTIONS; MATLAB;

EID: 85162445285     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (222)

References (35)
  • 1
    • 85162069624 scopus 로고    scopus 로고
    • Phone recognition with the mean-covariance restricted Boltzmann machine
    • G. E. Dahl, M. Ranzato, A. Mohamed, and G. E. Hinton. Phone recognition with the mean-covariance restricted Boltzmann machine. In NIPS. 2010.
    • (2010) NIPS
    • Dahl, G.E.1    Ranzato, M.2    Mohamed, A.3    Hinton, G.E.4
  • 2
    • 84863380535 scopus 로고    scopus 로고
    • Unsupervised feature learning for audio classification using convolutional deep belief networks
    • H. Lee, Y. Largman, P. Pham, and A. Y. Ng. Unsupervised feature learning for audio classification using convolutional deep belief networks. In NIPS. 2009.
    • (2009) NIPS
    • Lee, H.1    Largman, Y.2    Pham, P.3    Ng, A.Y.4
  • 3
    • 70450209196 scopus 로고    scopus 로고
    • Linear spatial pyramid matching using sparse coding for image classification
    • J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR, 2009.
    • (2009) CVPR
    • Yang, J.1    Yu, K.2    Gong, Y.3    Huang, T.4
  • 4
    • 34948870900 scopus 로고    scopus 로고
    • Unsupervised learning of invariant feature hierarchies with applications to object recognition
    • M.A. Ranzato, F. J. Huang, Y.-L. Boureau, and Y. LeCun. Unsupervised learning of invariant feature hierarchies with applications to object recognition. In CVPR, 2007.
    • (2007) CVPR
    • Ranzato, M.A.1    Huang, F.J.2    Boureau, Y.-L.3    Lecun, Y.4
  • 5
    • 80052874098 scopus 로고    scopus 로고
    • Learning hierarchical spatio-temporal features for action recognition with independent subspace analysis
    • Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng. Learning hierarchical spatio-temporal features for action recognition with independent subspace analysis. In CVPR, 2011.
    • (2011) CVPR
    • Le, Q.V.1    Zou, W.Y.2    Yeung, S.Y.3    Ng, A.Y.4
  • 6
    • 85161980001 scopus 로고    scopus 로고
    • Sparse deep belief net model for visual area v2
    • H. Lee, C. Ekanadham, and A.Y. Ng. Sparse deep belief net model for visual area v2. In NIPS, 2008.
    • (2008) NIPS
    • Lee, H.1    Ekanadham, C.2    Ng, A.Y.3
  • 7
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • G. E. Hinton, S.Osindero, and Y.W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006.
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.W.3
  • 8
    • 56449089103 scopus 로고    scopus 로고
    • Extracting and composing robust features with denoising autoencoders
    • P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol. Extracting and composing robust features with denoising autoencoders. In ICML, 2008.
    • (2008) ICML
    • Vincent, P.1    Larochelle, H.2    Bengio, Y.3    Manzagol, P.A.4
  • 9
    • 0032492432 scopus 로고    scopus 로고
    • Independent component filters of natural images compared with simple cells in primary visual cortex
    • J. H. van Hateren and A. van der Schaaf. Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings: Biological Sciences, 265(1394):359-366, 1998.
    • (1998) Proceedings: Biological Sciences , vol.265 , Issue.1394 , pp. 359-366
    • Van Hateren, J.H.1    Van Der Schaaf, A.2
  • 10
    • 0030832881 scopus 로고    scopus 로고
    • The "independent components" of natural scenes are edge filters
    • December
    • A. J. Bell and T. J. Sejnowski. The "independent components" of natural scenes are edge filters. Vision Res., 37(23):3327-3338, December 1997.
    • (1997) Vision Res. , vol.37 , Issue.23 , pp. 3327-3338
    • Bell, A.J.1    Sejnowski, T.J.2
  • 11
    • 85162476392 scopus 로고    scopus 로고
    • Sparse coding with an overcomplete basis set: A strategy employed by V1?
    • B.Olshausen and D. Field. Sparse coding with an overcomplete basis set: A strategy employed by V1? Nature, 1997.
    • (1997) Nature
    • Olshausen, B.1    Field, D.2
  • 13
    • 0000929221 scopus 로고
    • What is the goal of sensory coding?
    • July
    • D. J. Field. What is the goal of sensory coding? Neural Computation, 6(4):559-601, July 1994.
    • (1994) Neural Computation , vol.6 , Issue.4 , pp. 559-601
    • Field, D.J.1
  • 14
    • 0005713456 scopus 로고    scopus 로고
    • Characterizing the sparseness of neural codes
    • January
    • B. Willmore and D. J. Tolhurst. Characterizing the sparseness of neural codes. Network, 12(3):255-270, January 2001.
    • (2001) Network , vol.12 , Issue.3 , pp. 255-270
    • Willmore, B.1    Tolhurst, D.J.2
  • 15
    • 0034939633 scopus 로고    scopus 로고
    • Natural signal statistics and sensory gain control
    • O. Schwartz and E. P. Simoncelli. Natural signal statistics and sensory gain control. Nature Neuroscience, 4:819-825, 2001.
    • (2001) Nature Neuroscience , vol.4 , pp. 819-825
    • Schwartz, O.1    Simoncelli, E.P.2
  • 16
    • 85112276587 scopus 로고    scopus 로고
    • Efficient learning of sparse representations with an energy-based model
    • M.A. Ranzato, C. Poultney, S. Chopra, and Y. Lecun. Efficient learning of sparse representations with an energy-based model. In NIPS, 2006.
    • (2006) NIPS
    • Ranzato, M.A.1    Poultney, C.2    Chopra, S.3    Lecun, Y.4
  • 17
    • 80053446757 scopus 로고    scopus 로고
    • An analysis of single-layer networks in unsupervised feature learning
    • A. Coates, H. Lee, and A. Y. Ng. An analysis of single-layer networks in unsupervised feature learning. In AISTATS, 2011.
    • (2011) AISTATS
    • Coates, A.1    Lee, H.2    Ng, A.Y.3
  • 18
    • 0000216612 scopus 로고
    • What determines the capacity of autoassociative memories in the brain?
    • A. Treves and E. Rolls. What determines the capacity of autoassociative memories in the brain? Network: Computation in Neural Systems, 2:371-397(27), 1991.
    • (1991) Network: Computation in Neural Systems , vol.2 , Issue.27 , pp. 371-397
    • Treves, A.1    Rolls, E.2
  • 20
    • 85162517127 scopus 로고    scopus 로고
    • M. Schmidt. minFunc. http://www.cs.ubc.ca/~schmidtm/Software/minFunc. html, 2005.
    • (2005)
    • Schmidt, M.1
  • 21
    • 77955989954 scopus 로고    scopus 로고
    • Modeling pixel means and covariances using factorized third-order boltzmann machines
    • M. Ranzato and G. E. Hinton. Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines. In CVPR, 2010.
    • (2010) CVPR
    • Ranzato, M.1    Hinton, G.E.2
  • 22
    • 78149334300 scopus 로고    scopus 로고
    • A two-layer model of natural stimuli estimated with score matching
    • U. Köster and A. Hyvärinen. A two-layer model of natural stimuli estimated with score matching. Neural Computation, 22(9):2308-2333, 2010.
    • (2010) Neural Computation , vol.22 , Issue.9 , pp. 2308-2333
    • Köster, U.1    Hyvärinen, A.2
  • 25
    • 33947686745 scopus 로고    scopus 로고
    • Large margin gaussian mixture modeling for phonetic classification and recognition
    • IEEE
    • F. Sha and L.K. Saul. Large margin gaussian mixture modeling for phonetic classification and recognition. In ICASSP. IEEE, 2006.
    • (2006) ICASSP
    • Sha, F.1    Saul, L.K.2
  • 26
    • 70450164063 scopus 로고    scopus 로고
    • Hidden conditional random field with distribution constraints for phone classification
    • D. Yu, L. Deng, and A. Acero. Hidden conditional random field with distribution constraints for phone classification. In Interspeech, 2009.
    • (2009) Interspeech
    • Yu, D.1    Deng, L.2    Acero, A.3
  • 30
    • 71149119964 scopus 로고    scopus 로고
    • Online dictionary learning for sparse coding
    • J. Mairal, F. Bach, J. Ponce, and G. Sapiro.Online dictionary learning for sparse coding. In ICML, 2009.
    • (2009) ICML
    • Mairal, J.1    Bach, F.2    Ponce, J.3    Sapiro, G.4
  • 31
    • 79955702502 scopus 로고    scopus 로고
    • LIBSVM: A library for support vector machines
    • Software
    • C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
    • (2011) ACM Transactions on Intelligent Systems and Technology , vol.2 , pp. 271-2727
    • Chang, C.-C.1    Lin, C.-J.2
  • 33
    • 77953183471 scopus 로고    scopus 로고
    • What is the best multi-stage architecture for object recognition?
    • K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun. What is the best multi-stage architecture for object recognition? In ICCV, 2009.
    • (2009) ICCV
    • Jarrett, K.1    Kavukcuoglu, K.2    Ranzato, M.3    Lecun, Y.4
  • 34
    • 38949193299 scopus 로고    scopus 로고
    • Why is Real-World visual object recognition hard?
    • January
    • N. Pinto, D. D. Cox, and J. J. DiCarlo. Why is Real-World visual object recognition hard? PLoS Comput Biol, 4(1):e27+, January 2008.
    • (2008) PLoS Comput Biol , vol.4 , Issue.1
    • Pinto, N.1    Cox, D.D.2    Dicarlo, J.J.3
  • 35
    • 84900510076 scopus 로고    scopus 로고
    • Non-negative matrix factorization with sparseness constraints
    • PatrikO. Hoyer. Non-negative matrix factorization with sparseness constraints. JMLR, 5:1457-1469, 2004.
    • (2004) JMLR , vol.5 , pp. 1457-1469
    • Hoyer, PatrikO.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.