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Volumn 2018-December, Issue , 2018, Pages 9516-9527

Probabilistic model-agnostic meta-learning

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); LARGE DATASET; PROBABILITY DISTRIBUTIONS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

EID: 85064463171     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (651)

References (43)
  • 18
    • 0027803368 scopus 로고
    • Keeping the neural networks simple by minimizing the description length of the weights
    • G. E. Hinton and D. Van Camp. Keeping the neural networks simple by minimizing the description length of the weights. In Conference on Computational learning theory, 1993.
    • (1993) Conference on Computational Learning Theory
    • Hinton, G.E.1    Van Camp, D.2
  • 24
    • 84949683101 scopus 로고    scopus 로고
    • Human-level concept learning through probabilistic program induction
    • B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 2015.
    • (2015) Science
    • Lake, B.M.1    Salakhutdinov, R.2    Tenenbaum, J.B.3
  • 27
    • 0001025418 scopus 로고
    • A practical Bayesian framework for backpropagation networks
    • D. J. MacKay. A practical Bayesian framework for backpropagation networks. Neural computation, 1992.
    • (1992) Neural Computation
    • MacKay, D.J.1
  • 34
    • 0030546173 scopus 로고    scopus 로고
    • Equivalence of regularization and truncated iteration for general ill-posed problems
    • R. J. Santos. Equivalence of regularization and truncated iteration for general ill-posed problems. Linear Algebra and its Applications, 1996.
    • (1996) Linear Algebra and Its Applications
    • Santos, R.J.1
  • 38
    • 85063581516 scopus 로고    scopus 로고
    • Learning to compare: Relation network for few-shot learning
    • abs/1711.06025
    • F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. S. Torr, and T. M. Hospedales. Learning to compare: Relation network for few-shot learning. CoRR, abs/1711.06025, 2017. URL http://arxiv.org/abs/1711.06025.
    • (2017) CoRR
    • Sung, F.1    Yang, Y.2    Zhang, L.3    Xiang, T.4    Torr, P.H.S.5    Hospedales, T.M.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.