메뉴 건너뛰기




Volumn , Issue , 2017, Pages

Distributed second-order optimization using Kronecker-factored approximations

Author keywords

[No Author keywords available]

Indexed keywords

GRADIENT METHODS; IMAGE ENHANCEMENT; STOCHASTIC SYSTEMS;

EID: 85057316160     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (98)

References (40)
  • 2
    • 0000396062 scopus 로고    scopus 로고
    • Natural gradient works efficiently in learning
    • Shun-Ichi Amari. Natural gradient works efficiently in learning. Neural computation, 10(2):251-276, 1998.
    • (1998) Neural Computation , vol.10 , Issue.2 , pp. 251-276
    • Amari, S.-I.1
  • 4
    • 68949096711 scopus 로고    scopus 로고
    • SGD-QN: Careful quasi-Newton stochastic gradient descent
    • Jul
    • Antoine Bordes, Léon Bottou, and Patrick Gallinari. Sgd-qn: Careful quasi-newton stochastic gradient descent. Journal of Machine Learning Research, 10(Jul):1737-1754, 2009.
    • (2009) Journal of Machine Learning Research , vol.10 , pp. 1737-1754
    • Bordes, A.1    Bottou, L.2    Gallinari, P.3
  • 5
    • 84976910543 scopus 로고    scopus 로고
    • A stochastic quasi-Newton method for large-scale optimization
    • Richard H Byrd, SL Hansen, Jorge Nocedal, and Yoram Singer. A stochastic quasi-newton method for large-scale optimization. SIAM Journal on Optimization, 26(2):1008-1031, 2016.
    • (2016) SIAM Journal on Optimization , vol.26 , Issue.2 , pp. 1008-1031
    • Byrd, R.H.1    Hansen, S.L.2    Nocedal, J.3    Singer, Y.4
  • 6
    • 84965175669 scopus 로고    scopus 로고
    • Hessian-free optimization for learning deep multidimensional recurrent neural networks
    • Minhyung Cho, Chandra Dhir, and Jaehyung Lee. Hessian-free optimization for learning deep multidimensional recurrent neural networks. In Advances in Neural Information Processing Systems, pages 883-891, 2015.
    • (2015) Advances in Neural Information Processing Systems , pp. 883-891
    • Cho, M.1    Dhir, C.2    Lee, J.3
  • 10
    • 80052250414 scopus 로고    scopus 로고
    • Adaptive subgradient methods for online learning and stochastic optimization
    • Jul
    • John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12(Jul):2121-2159, 2011.
    • (2011) Journal of Machine Learning Research , vol.12 , pp. 2121-2159
    • Duchi, J.1    Hazan, E.2    Singer, Y.3
  • 15
    • 0034167148 scopus 로고    scopus 로고
    • On “natural” learning and pruning in multilayered perceptrons
    • Tom Heskes. On “natural” learning and pruning in multilayered perceptrons. Neural Computation, 12(4): 881-901, 2000.
    • (2000) Neural Computation , vol.12 , Issue.4 , pp. 881-901
    • Heskes, T.1
  • 23
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
    • (1998) Proceedings of the IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 27
    • 84872565347 scopus 로고    scopus 로고
    • Training deep and recurrent networks with Hessian-free optimization
    • Springer
    • James Martens and Ilya Sutskever. Training deep and recurrent networks with Hessian-free optimization. In Neural Networks: Tricks of the Trade, pages 479-535. Springer, 2012.
    • (2012) Neural Networks: Tricks of the Trade , pp. 479-535
    • Martens, J.1    Sutskever, I.2
  • 34
    • 0038231917 scopus 로고    scopus 로고
    • Centering neural network gradient factors
    • Genevieve B. Orr and Klaus-Robert Müller, editors, Springer Verlag, Berlin
    • Nicol N. Schraudolph. Centering neural network gradient factors. In Genevieve B. Orr and Klaus-Robert Müller, editors, Neural Networks: Tricks of the Trade, volume 1524 of Lecture Notes in Computer Science, pages 207-226. Springer Verlag, Berlin, 1998.
    • (1998) Neural Networks: Tricks of the Trade, 1524 of Lecture Notes in Computer Science , pp. 207-226
    • Schraudolph, N.N.1
  • 35
    • 0036631778 scopus 로고    scopus 로고
    • Fast curvature matrix-vector products for second-order gradient descent
    • Nicol N. Schraudolph. Fast curvature matrix-vector products for second-order gradient descent. Neural Computation, 14(7), 2002.
    • (2002) Neural Computation , vol.14 , Issue.7
    • Schraudolph, N.N.1
  • 36
    • 72449211086 scopus 로고    scopus 로고
    • A stochastic quasi-Newton method for online convex optimization
    • Nicol N Schraudolph, Jin Yu, Simon Günter, et al. A stochastic quasi-newton method for online convex optimization. In AISTATS, volume 7, pages 436-443, 2007.
    • (2007) AISTATS , vol.7 , pp. 436-443
    • Schraudolph, N.N.1    Yu, J.2    Günter, S.3
  • 39
    • 84954239313 scopus 로고    scopus 로고
    • Krylov subspace descent for deep learning
    • Oriol Vinyals and Daniel Povey. Krylov subspace descent for deep learning. In AISTATS, pages 1261-1268, 2012.
    • (2012) AISTATS , pp. 1261-1268
    • Vinyals, O.1    Povey, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.