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Volumn , Issue , 2009, Pages 273-280

Accelerated sampling for the Indian buffet process

Author keywords

[No Author keywords available]

Indexed keywords

GIBBS SAMPLERS; INFERENCE TECHNIQUES; NON-PARAMETRIC; REAL-WORLD DATASETS; RUNNING TIME;

EID: 71149094085     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (46)

References (14)
  • 1
    • 34547466115 scopus 로고    scopus 로고
    • Identifying protein complexes in high-throughput protein interaction screens using an infinite latent feature model
    • Chu, W., Ghahramani, Z., Krause, R., & Wild, D. (2006). Identifying protein complexes in high-throughput protein interaction screens using an infinite latent feature model. Pacific Symposium on Biocomputing (pp. 231-242).
    • (2006) Pacific Symposium on Biocomputing , pp. 231-242
    • Chu, W.1    Ghahramani, Z.2    Krause, R.3    Wild, D.4
  • 4
    • 0035363672 scopus 로고    scopus 로고
    • From few to many: Illumination cone models for face recognition under variable lighting and pose
    • Georghiades, A., Belhumeur, P., & Kriegman, D. (2001). From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence, 23, 643-660.
    • (2001) IEEE Trans. Pattern Anal. Mach. Intelligence , vol.23 , pp. 643-660
    • Georghiades, A.1    Belhumeur, P.2    Kriegman, D.3
  • 6
    • 33645039209 scopus 로고    scopus 로고
    • Infinite latent feature models and the Indian buffet process
    • Gatsby Comp. Neuroscience Unit
    • Griffiths, T., & Ghahramani, Z. (2005). Infinite latent feature models and the Indian buffet process. TR 2005-001, Gatsby Comp. Neuroscience Unit.
    • (2005) TR , pp. 2005-3001
    • Griffiths, T.1    Ghahramani, Z.2
  • 10
    • 55749098088 scopus 로고    scopus 로고
    • Latent features in similarity judgments: A nonparametric Bayesian approach
    • Navarro, D. J., & Griffiths, T. L. (2008). Latent features in similarity judgments: A nonparametric Bayesian approach. Neural Comp., 20, 2597-2628.
    • (2008) Neural Comp , vol.20 , pp. 2597-2628
    • Navarro, D.J.1    Griffiths, T.L.2
  • 11
    • 33846199251 scopus 로고    scopus 로고
    • A discriminative model for polyphonic piano transcription
    • Poliner, G. E., & Ellis, D. P. W. (2007). A discriminative model for polyphonic piano transcription. EURASIP J. Appl. Signal Process., 2007, 154-154.
    • (2007) EURASIP J. Appl. Signal Process , vol.2007 , pp. 154-154
    • Poliner, G.E.1    Ellis, D.P.W.2
  • 13
    • 65549098786 scopus 로고    scopus 로고
    • Bayesian K-means as a "maximization-expectation" algorithm
    • Welling, M., & Kurihara, K. (2009). Bayesian K-means as a "maximization-expectation" algorithm. Neural Computation (pp. 1145-1172).
    • (2009) Neural Computation , pp. 1145-1172
    • Welling, M.1    Kurihara, K.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.