메뉴 건너뛰기




Volumn 2017-December, Issue , 2017, Pages 2733-2742

Variational inference via χ upper bound minimization

Author keywords

[No Author keywords available]

Indexed keywords

MARKOV PROCESSES; UNCERTAINTY ANALYSIS;

EID: 85047018952     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (151)

References (35)
  • 2
    • 0033225865 scopus 로고    scopus 로고
    • Introduction to variational methods for graphical models
    • M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. Introduction to variational methods for graphical models. Machine Learning, 37:183-233, 1999.
    • (1999) Machine Learning , vol.37 , pp. 183-233
    • Jordan, M.1    Ghahramani, Z.2    Jaakkola, T.3    Saul, L.4
  • 8
    • 25444528713 scopus 로고    scopus 로고
    • Assessing approximate inference for binary Gaussian process classification
    • M. Kuss and C. E. Rasmussen. Assessing approximate inference for binary Gaussian process classification. JMLR, 6:1679-1704, 2005.
    • (2005) JMLR , vol.6 , pp. 1679-1704
    • Kuss, M.1    Rasmussen, C.E.2
  • 10
    • 0001025418 scopus 로고
    • Bayesian interpolation
    • D. J. C. MacKay. Bayesian interpolation. Neural Computation, 4(3):415-447, 1992.
    • (1992) Neural Computation , vol.4 , Issue.3 , pp. 415-447
    • MacKay, D.J.C.1
  • 13
    • 85083952489 scopus 로고    scopus 로고
    • Auto-encoding variational bayes
    • D. P. Kingma and M. Welling. Auto-encoding variational Bayes. In ICLR, 2014.
    • (2014) ICLR
    • Kingma, D.P.1    Welling, M.2
  • 14
    • 84919796093 scopus 로고    scopus 로고
    • Stochastic backpropagation and approximate inference in deep generative models
    • D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In ICML, 2014.
    • (2014) ICML
    • Rezende, D.J.1    Mohamed, S.2    Wierstra, D.3
  • 15
    • 0034320350 scopus 로고    scopus 로고
    • Gaussian processes for classification: Mean-field algorithms
    • M. Opper and O. Winther. Gaussian processes for classification: Mean-field algorithms. Neural Computation, 12(11):2655-2684, 2000.
    • (2000) Neural Computation , vol.12 , Issue.11 , pp. 2655-2684
    • Opper, M.1    Winther, O.2
  • 19
    • 27744528998 scopus 로고    scopus 로고
    • Technical report, Microsoft Research
    • T. Minka. Power EP. Technical report, Microsoft Research, 2004.
    • (2004) Power EP
    • Minka, T.1
  • 21
    • 85031094563 scopus 로고    scopus 로고
    • Variational inference with Rényi divergence
    • Y. Li and R. E. Turner. Variational inference with Rényi divergence. In NIPS, 2016.
    • (2016) NIPS
    • Li, Y.1    Turner, R.E.2
  • 23
    • 85047001143 scopus 로고    scopus 로고
    • Neural variational inference and learning in undirected graphical models
    • Volodymyr Kuleshov and Stefano Ermon. Neural variational inference and learning in undirected graphical models. In NIPS, 2017.
    • (2017) NIPS
    • Kuleshov, V.1    Ermon, S.2
  • 24
    • 0033225865 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. Machine Learning, 37(2):183-233, 1999.
    • (1999) Machine Learning , vol.37 , Issue.2 , pp. 183-233
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.S.3    Saul, L.K.4
  • 29
    • 0001201909 scopus 로고
    • Bayesian model selection in social research
    • A. E. Raftery. Bayesian model selection in social research. Sociological methodology, 25:111-164, 1995.
    • (1995) Sociological Methodology , vol.25 , pp. 111-164
    • Raftery, A.E.1
  • 30
    • 84867133463 scopus 로고    scopus 로고
    • Variational Bayesian inference with stochastic search
    • J. Paisley, D. Blei, and M. Jordan. Variational Bayesian inference with stochastic search. In ICML, 2012.
    • (2012) ICML
    • Paisley, J.1    Blei, D.2    Jordan, M.3
  • 31
    • 85046996170 scopus 로고    scopus 로고
    • Expectation propagation in the large-data limit
    • G. Dehaene and S. Barthelmé. Expectation propagation in the large-data limit. In NIPS, 2015.
    • (2015) NIPS
    • Dehaene, G.1    Barthelmé, S.2
  • 35
    • 84919795562 scopus 로고    scopus 로고
    • Factorized point process intensities: A spatial analysis of professional basketball
    • A. Miller, L. Bornn, R. Adams, and K. Goldsberry. Factorized point process intensities: A spatial analysis of professional basketball. In ICML, 2014.
    • (2014) ICML
    • Miller, A.1    Bornn, L.2    Adams, R.3    Goldsberry, K.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.