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Volumn 2015-January, Issue , 2015, Pages 3438-3446

Bayesian dark knowledge

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; INFORMATION SCIENCE; NEURAL NETWORKS; STOCHASTIC SYSTEMS;

EID: 84965170844     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (244)

References (19)
  • 1
    • 84867125855 scopus 로고    scopus 로고
    • Bayesian posterior sampling via stochastic gradient fisher scoring
    • [AKW12] S. Ahn, A. Korattikara, and M. Welling. Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring. In ICML, 2012.
    • (2012) ICML
    • Ahn, S.1    Korattikara, A.2    Welling, M.3
  • 2
    • 84919919193 scopus 로고    scopus 로고
    • Distributed stochastic gradient MCMC
    • [ASW14] Sungjin Ahn, Babak Shahbaba, and Max Welling. Distributed stochastic gradient MCMC. In ICML, 2014.
    • (2014) ICML
    • Ahn, S.1    Shahbaba, B.2    Welling, M.3
  • 5
    • 61449167850 scopus 로고    scopus 로고
    • Some comparisons among quadratic, spherical, and logarithmic scoring rules
    • [Bic07] J Eric Bickel. Some comparisons among quadratic, spherical, and logarithmic scoring rules. Decision Analysis, 4(2):49-65, 2007.
    • (2007) Decision Analysis , vol.4 , Issue.2 , pp. 49-65
    • Eric Bickel, J.1
  • 6
    • 84919787787 scopus 로고    scopus 로고
    • Stochastic gradient hamiltonian Monte Carlo
    • [CFG14] Tianqi Chen, Emily B Fox, and Carlos Guestrin. Stochastic Gradient Hamiltonian Monte Carlo. In ICML, 2014.
    • (2014) ICML
    • Chen, T.1    Fox, E.B.2    Guestrin, C.3
  • 9
    • 85162557101 scopus 로고    scopus 로고
    • Practical variational inference for neural networks
    • [Gra11] Alex Graves. Practical variational inference for neural networks. In NIPS, 2011.
    • (2011) NIPS
    • Graves, A.1
  • 10
    • 84969909658 scopus 로고    scopus 로고
    • Probabilistic backpropagation for scalable learning of Bayesian neural networks
    • [HLA15] J. Hernández-Lobato and R. Adams. Probabilistic backpropagation for scalable learning of bayesian neural networks. In ICML, 2015.
    • (2015) ICML
    • Hernández-Lobato, J.1    Adams, R.2
  • 12
    • 84919810317 scopus 로고    scopus 로고
    • Stochastic gradient VB and the variational auto-encoder
    • [KW14] Diederik P Kingma and Max Welling. Stochastic gradient VB and the variational auto-encoder. In ICLR, 2014.
    • (2014) ICLR
    • Kingma, D.P.1    Welling, M.2
  • 14
    • 84898939739 scopus 로고    scopus 로고
    • Stochastic gradient riemannian langevin dynamics on the probability simplex
    • [PT13] Sam Patterson and Yee Whye Teh. Stochastic gradient riemannian langevin dynamics on the probability simplex. In NIPS, 2013.
    • (2013) NIPS
    • Patterson, S.1    Teh, Y.W.2
  • 16
    • 84919796093 scopus 로고    scopus 로고
    • Stochastic backpropagation and approximate inference in deep generative models
    • [RMW14] D. Rezende, S. Mohamed, and D. Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In ICML, 2014.
    • (2014) ICML
    • Rezende, D.1    Mohamed, S.2    Wierstra, D.3
  • 17
    • 31844439545 scopus 로고    scopus 로고
    • Compact approximations to Bayesian predictive distributions
    • [SG05] Edward Snelson and Zoubin Ghahramani. Compact approximations to bayesian predictive distributions. In ICML, 2005.
    • (2005) ICML
    • Snelson, E.1    Ghahramani, Z.2
  • 19
    • 80053452150 scopus 로고    scopus 로고
    • Bayesian learning via stochastic gradient Langevin dynamics
    • [WT11] Max Welling and Yee W Teh. Bayesian learning via stochastic gradient Langevin dynamics. In ICML, 2011.
    • (2011) ICML
    • Welling, M.1    Teh, Y.W.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.