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Volumn 2, Issue , 2012, Pages 1349-1357

Compressive neural representation of sparse, high-dimensional probabilities

Author keywords

[No Author keywords available]

Indexed keywords

COMPRESSIVE MEASUREMENTS; COMPRESSIVE SENSING; GEOMETRIC STRUCTURE; JOINT DISTRIBUTIONS; NEURAL REPRESENTATIONS; NONLINEAR FUNCTIONS; NOVEL APPLICATIONS; PROBABILISTIC COMPUTATION;

EID: 84877726667     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (6)

References (23)
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    • Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment
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    • Berkes, P.1    Orbán, G.2    Lengyel, M.3    Fiser, J.4
  • 3
    • 31744440684 scopus 로고    scopus 로고
    • Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
    • Candès E, Romberg J, Tao T (2006) Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52: 489-509.
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    • Candès, E.1    Romberg, J.2    Tao, T.3
  • 4
    • 33947416035 scopus 로고    scopus 로고
    • Near-optimal signal recovery from random projections: Universal encoding strategies?
    • Candès E, Tao T (2006) Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory 52: 5406-5425.
    • (2006) IEEE Transactions on Information Theory , vol.52 , pp. 5406-5425
    • Candès, E.1    Tao, T.2
  • 6
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    • A probabilistic and RIPless theory of compressed sensing
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    • Candès, E.1    Plan, Y.2
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    • Construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property
    • Calderbank R, Howard S, Jafarpour S (2010) Construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property. Selected Topics in Signal Processing 4: 358-374.
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    • Calderbank, R.1    Howard, S.2    Jafarpour, S.3
  • 13
    • 0242511426 scopus 로고    scopus 로고
    • Bounds for the number of threshold functions
    • Irmatov A (2009) Bounds for the number of threshold functions. Discrete Mathematics and Applications 6: 569-583.
    • (2009) Discrete Mathematics and Applications , vol.6 , pp. 569-583
    • Irmatov, A.1
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    • Time-dependent statistics of the Ising model
    • Glauber RJ (1963) Time-dependent statistics of the Ising model. Journal of Mathematical Physics 4: 294-307.
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  • 16
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    • Yang J, Zhang Y (2011) Alternating direction algorithms for L1 problems in compressive sensing. SIAM Journal on Scientific Computing 33: 250-278.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.