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Volumn , Issue , 2001, Pages

An information maximization approach to overcomplete and recurrent representations

Author keywords

[No Author keywords available]

Indexed keywords

FEEDFORWARD CONNECTIONS; INDEPENDENT COMPONENTS; INFORMATION MAXIMIZATION; LEARNING RULES; MUTUAL INFORMATIONS; OVER-COMPLETE;

EID: 84898980111     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (15)

References (11)
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    • Jutten, C & Hérault, J (1991). Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing 24, 1-10.
    • (1991) Signal Processing , vol.24 , pp. 1-10
    • Jutten, C.1    Hérault, J.2
  • 4
    • 0031590130 scopus 로고    scopus 로고
    • Generative models for discovering sparse distributed representations
    • Hinton, G & Ghahramani, Z (1997). Generative models for discovering sparse distributed representations. Philosophical Transactions Royal Society B 352, 1177-1190.
    • (1997) Philosophical Transactions Royal Society B , vol.352 , pp. 1177-1190
    • Hinton, G.1    Ghahramani, Z.2
  • 5
    • 0002327756 scopus 로고    scopus 로고
    • A context-sensitive generalization of ICA
    • Pearlmutter, B & Parra, L (1996). A context-sensitive generalization of ICA. In ICONIP'96, 151-157.
    • (1996) ICONIP'96 , pp. 151-157
    • Pearlmutter, B.1    Parra, L.2
  • 6
    • 0029411030 scopus 로고
    • An information maximization approach to blind separation and blind deconvolution
    • Bell, AJ & Sejnowski, TJ (1995). An information maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129-1159.
    • (1995) Neural Comput. , vol.7 , pp. 1129-1159
    • Bell, A.J.1    Sejnowski, T.J.2
  • 7
    • 0001471775 scopus 로고
    • Unsupervised learning
    • Barlow, HB (1989). Unsupervised learning. Neural Comput. 1, 295-311.
    • (1989) Neural Comput. , vol.1 , pp. 295-311
    • Barlow, H.B.1
  • 8
    • 0001525549 scopus 로고
    • Local synaptic learning rules suffice to maximize mutual information in a linear network
    • Linsker, R (1992). Local synaptic learning rules suffice to maximize mutual information in a linear network. Neural Comput. 4, 691-702.
    • (1992) Neural Comput. , vol.4 , pp. 691-702
    • Linsker, R.1
  • 9
    • 0000184052 scopus 로고    scopus 로고
    • Statistical independence and novelty detection with information preserving nonlinear maps
    • Parra, L, Deco, G, & Miesbach, S (1996). Statistical independence and novelty detection with information preserving nonlinear maps. Neural Comput. 8, 260-269.
    • (1996) Neural Comput. , vol.8 , pp. 260-269
    • Parra, L.1    Deco, G.2    Miesbach, S.3
  • 11


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.