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Volumn , Issue , 2002, Pages 1359-1366

Learning Sparse Topographic Representations with Products of Student-t Distributions

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; MAXIMUM LIKELIHOOD;

EID: 79953038910     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (50)

References (9)
  • 1
    • 0013344078 scopus 로고    scopus 로고
    • Training products of experts by minimizing contrastive divergence
    • G.E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14:1771-1800, 2002.
    • (2002) Neural Computation , vol.14 , pp. 1771-1800
    • Hinton, G.E.1
  • 3
    • 0348223982 scopus 로고    scopus 로고
    • Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation
    • A. Hyvarinen. Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation. Neural Computation, 11(7):1739-1768, 1999.
    • (1999) Neural Computation , vol.11 , Issue.7 , pp. 1739-1768
    • Hyvarinen, A.1
  • 4
    • 0035409349 scopus 로고    scopus 로고
    • Topographic independent component analysis
    • A. Hyvarinen, P.O. Hoyer, and M. Inki. Topographic independent component analysis. Neural Computation, 13(7):1525-1558, 2001.
    • (2001) Neural Computation , vol.13 , Issue.7 , pp. 1525-1558
    • Hyvarinen, A.1    Hoyer, P.O.2    Inki, M.3
  • 6
    • 0033318388 scopus 로고    scopus 로고
    • Modeling the joint statistics of images in the wavelet domain
    • pages Denver
    • E.P. Simoncelli. Modeling the joint statistics of images in the wavelet domain. In Proc SPIE, 44th Annual Meeting, volume 3813, pages 188-195, Denver, 1999.
    • (1999) Proc SPIE, 44th Annual Meeting , vol.3813 , pp. 188-195
    • Simoncelli, E.P.1
  • 7
    • 0034430087 scopus 로고    scopus 로고
    • Image denoising using a local Gaussian scale mixture model in the wavelet domain
    • San Diego
    • V. Strela, J. Portilla, and E. Simoncelli. Image denoising using a local Gaussian scale mixture model in the wavelet domain. In Proc. SPIE, 45th Annual Meeting, San Diego, 2000.
    • (2000) Proc. SPIE, 45th Annual Meeting
    • Strela, V.1    Portilla, J.2    Simoncelli, E.3
  • 9
    • 0000806445 scopus 로고    scopus 로고
    • Minimax entropy principle and its application to texture modeling
    • S.C. Zhu, Z.N. Wu, and D. Mumford. Minimax entropy principle and its application to texture modeling. Neural Computation, 9(8):1627-1660, 1997.
    • (1997) Neural Computation , vol.9 , Issue.8 , pp. 1627-1660
    • Zhu, S.C.1    Wu, Z.N.2    Mumford, D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.