確率的勾配降下法 (Japanese Wikipedia)

Analysis of information sources in references of the Wikipedia article "確率的勾配降下法" in Japanese language version.

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arxiv.org

Matthew D. Zeiler (2012). ADADELTA: An Adaptive Learning Rate Method. http://arxiv.org/abs/1212.5701.
Jascha Sohl-Dickstein; Ben Poole; Surya Ganguli (2014). “Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods”. Proceedings of the 31 st International Conference on Machine Learning 32. http://arxiv.org/abs/1311.2115.
Diederik Kingma; Jimmy Ba (2015). “Adam: A Method for Stochastic Optimization”. Proceedings of the 3rd International Conference for Learning Representations, San Diego. http://arxiv.org/abs/1412.6980.
Stephane Ross; Paul Mineiro; John Langford (2013). “Normalized Online Learning”. Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence. http://arxiv.org/abs/1305.6646.

bottou.org

leon.bottou.org

Bottou, Léon; Bousquet, Olivier (2008). The Tradeoffs of Large Scale Learning. Advances in Neural Information Processing Systems. Vol. 20. pp. 161–168.
Bottou, Léon (1998). “Online Algorithms and Stochastic Approximations”. Online Learning and Neural Networks. Cambridge University Press. ISBN 978-0-521-65263-6

cmu.edu

ark.cs.cmu.edu

Perla, Joseph (2013). Notes on AdaGrad.

cornell.edu

ecommons.cornell.edu

David Ruppert (1988). “Efficient estimations from a slowly convergent Robbins-Monro process”. Technical Report (Cornell University Operations Research and Industrial Engineering) 781.

doi.org

Ferguson, Thomas S. (1982). “An inconsistent maximum likelihood estimate”. Journal of the American Statistical Association 77 (380): 831–834. doi:10.1080/01621459.1982.10477894. JSTOR 2287314.
Kiwiel, Krzysztof C. (2001年). “Convergence and efficiency of subgradient methods for quasiconvex minimization”. Mathematical Programming (Series A) (Berlin, Heidelberg: Springer) 90 (1): pp. 1–25. doi:10.1007/PL00011414. ISSN 0025-5610
Herbert Robbins; Sutton Monro (1951). “A Stochastic Approximation Method”. Ann. Math. Statist. 22 (3): 400-407. doi:10.1214/aoms/1177729586.
Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (8 October 1986). “Learning representations by back-propagating errors”. Nature 323 (6088): 533–536. doi:10.1038/323533a0.

jmlr.org

John Langford; Lihong Li; Tong Zhang (2009). “Sparse Online Learning via Truncated Gradient”. Journal of Machine Learning Research 10: 777-801.
John Duchi; Elad Hazan; Yoram Singer (2011). “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization”. The Journal of Machine Learning Research 12: 2121-2159.

jstor.org

Ferguson, Thomas S. (1982). “An inconsistent maximum likelihood estimate”. Journal of the American Statistical Association 77 (380): 831–834. doi:10.1080/01621459.1982.10477894. JSTOR 2287314.

luolc.com

Luo, Liangchen and Xiong, Yuanhao and Liu, Yan and Sun, Xu (2019). “Adaptive Gradient Methods with Dynamic Bound of Learning Rate”. Proceedings of the 7th International Conference on Learning Representations, New Orleans, Louisiana.

nips.cc

papers.nips.cc

Lin Xiao (2009). “Dual Averaging Method for Regularized Stochastic Learning and Online Optimization”. In Advances in Neural Information Processing Systems 23.

worldcat.org

search.worldcat.org

Kiwiel, Krzysztof C. (2001年). “Convergence and efficiency of subgradient methods for quasiconvex minimization”. Mathematical Programming (Series A) (Berlin, Heidelberg: Springer) 90 (1): pp. 1–25. doi:10.1007/PL00011414. ISSN 0025-5610