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Volumn 1, Issue , 2016, Pages 515-528

Hierarchical variational models

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; ARTIFICIAL INTELLIGENCE; EQUIVALENCE CLASSES; LEARNING SYSTEMS;

EID: 84997769529     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (123)

References (38)
  • 5
    • 10944265561 scopus 로고    scopus 로고
    • Helmholtz machines and wake-sleep learning
    • MIT Press, Cambridge, MA
    • Dayan, P. (2000). Helmholtz machines and wake-sleep learning. Handbook of Brain Theory and Neural Network. MIT Press, Cambridge, MA.
    • (2000) Handbook of Brain Theory and Neural Network
    • Dayan, P.1
  • 12
  • 14
    • 0001837853 scopus 로고    scopus 로고
    • Improving the mean field approximation via the use of mixture distributions
    • Springer Netherlands, Dordrecht
    • Jaakkola, T. S. and Jordan, M. I. (1998). Improving the Mean Field Approximation Via the Use of Mixture Distributions. In Learning in Graphical Models, pages 163-173. Springer Netherlands, Dordrecht.
    • (1998) Learning in Graphical Models , pp. 163-173
    • Jaakkola, T.S.1    Jordan, M.I.2
  • 15
    • 0033225865 scopus 로고    scopus 로고
    • Introduction to variational methods for graphical models
    • Jordan, M., Ghahramani, Z., Jaakkola, T., and Saul, L. (1999). Introduction to variational methods for graphical models. Machine Learning, 37:183-233.
    • (1999) Machine Learning , vol.37 , pp. 183-233
    • Jordan, M.1    Ghahramani, Z.2    Jaakkola, T.3    Saul, L.4
  • 21
    • 4243447828 scopus 로고
    • Technical Repport, Department of Computer Science, University of Toronto
    • Neal, R. (1990). Learning stochastic feedforward networks. Technical Repport CRG-TR-90-7: Department of Computer Science, University of Toronto.
    • (1990) Learning Stochastic Feedforward Networks
    • Neal, R.1
  • 28
    • 84891700107 scopus 로고    scopus 로고
    • Fixed-form variational posterior approximation through stochastic linear regression
    • Salimans, T., Knowles, D. A., et al. (2013). Fixed-form variational posterior approximation through stochastic linear regression. Bayesian Analysis, 8(4):837-882.
    • (2013) Bayesian Analysis , vol.8 , Issue.4 , pp. 837-882
    • Salimans, T.1    Knowles, D.A.2
  • 33
    • 84965132073 scopus 로고    scopus 로고
    • Local expectation gradients for doubly stochastic variational inference
    • Titsias, M. K. (2015). Local expectation gradients for doubly stochastic variational inference. In Neural Information Processing Systems.
    • (2015) Neural Information Processing Systems
    • Titsias, M.K.1
  • 37
    • 65749118363 scopus 로고    scopus 로고
    • Graphical models, exponential families, and variational inference
    • Wainwright, M. and Jordan, M. (2008). Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1(1-2):1-305.
    • (2008) Foundations and Trends in Machine Learning , vol.1 , Issue.1-2 , pp. 1-305
    • Wainwright, M.1    Jordan, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.