Discesa stocastica del gradiente (Italian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Discesa stocastica del gradiente" in Italian language version.

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Yoshua Bengio, Jérôme Louradour, Ronan Collobert e Jason Weston, Curriculum learning, ACM, 14 giugno 2009, pp. 41–48, DOI:10.1145/1553374.1553380, ISBN 978-1-60558-516-1.

archive.org

Yann Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli e Yoshua Bengio, Identifying and attacking the saddle point problem in high-dimensional non-convex optimization, 10 giugno 2014.
Matthew D. Zeiler, ADADELTA: An adaptive learning rate method, 2012.
Bengio, Y., Boulanger-Lewandowski, N. e Pascanu, R, Advances in Optimizing Recurrent Networks, 2012.
Zeiler, M. D., ADADELTA: An Adaptive Learning Rate Method, 2012.
Kingma Diederik e Jimmy Ba, Adam: A method for stochastic optimization, 2014.

arxiv.org

V. Patel, Kalman-Based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning, in SIAM Journal on Optimization, vol. 26, n. 4, 1º gennaio 2016, pp. 2620–2648, DOI:10.1137/15M1048239, ISSN 1052-6234 (WC · ACNP), arXiv:1512.01139.
(EN) Ollivier Yann, Online Natural Gradient as a Kalman Filter, 1º marzo 2017, arXiv:1703.00209.
Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach e James Martens, Adding Gradient Noise Improves Learning for Very Deep Networks, in arXiv:1511.06807 [cs, stat], 20 novembre 2015.
Wojciech Zaremba e Ilya Sutskever, Learning to Execute, in arXiv:1410.4615 [cs], 16 ottobre 2014.

bottou.org

leon.bottou.org

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

doi.org

dx.doi.org

Krzysztof C. Kiwiel, Convergence and efficiency of subgradient methods for quasiconvex minimization, in Mathematical Programming (Series A), vol. 90, n. 1, Berlin, Heidelberg, Springer, 2001, pp. 1–25, DOI:10.1007/PL00011414, ISSN 0025-5610 (WC · ACNP).
David E. Rumelhart, Hinton, Geoffrey E. e Williams, Ronald J., Learning representations by back-propagating errors, in Nature, vol. 323, n. 6088, 8 ottobre 1986, pp. 533–536, DOI:10.1038/323533a0.
V. Patel, Kalman-Based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning, in SIAM Journal on Optimization, vol. 26, n. 4, 1º gennaio 2016, pp. 2620–2648, DOI:10.1137/15M1048239, ISSN 1052-6234 (WC · ACNP), arXiv:1512.01139.
D. Bertsekas, Incremental Least Squares Methods and the Extended Kalman Filter, in SIAM Journal on Optimization, vol. 6, n. 3, 1º agosto 1996, pp. 807–822, DOI:10.1137/S1052623494268522, ISSN 1052-6234 (WC · ACNP).
Yoshua Bengio, Jérôme Louradour, Ronan Collobert e Jason Weston, Curriculum learning, ACM, 14 giugno 2009, pp. 41–48, DOI:10.1145/1553374.1553380, ISBN 978-1-60558-516-1.

googleblog.com

ai.googleblog.com

Derek Murray, Announcing TensorFlow 0.8 – now with distributed computing support!, su ai.googleblog.com, 13 aprile 2016.

ieee.org

ieeexplore.ieee.org

Cited by Christian Darken e John Moody, Fast adaptive k-means clustering: some empirical results, Int'l Joint Conf. on Neural Networks (IJCNN), IEEE, 1990.

lecun.com

yann.lecun.com

LeCun, Yann A., et al. "Efficient backprop." Neural networks: Tricks of the trade. Springer Berlin Heidelberg, 2012. 9-48

nih.gov

ncbi.nlm.nih.gov

A Cichocki, T Chen e S Amari, Stability Analysis of Learning Algorithms for Blind Source Separation., in Neural networks : the official journal of the International Neural Network Society, vol. 10, n. 8, novembre 1997, pp. 1345–1351, PMID 12662478.

oadoi.org

David E. Rumelhart, Hinton, Geoffrey E. e Williams, Ronald J., Learning representations by back-propagating errors, in Nature, vol. 323, n. 6088, 8 ottobre 1986, pp. 533–536, DOI:10.1038/323533a0.

openreview.net

Timothy Dozat, Incorporating Nesterov Momentum into Adam, 18 febbraio 2016.

ruder.io

An overview of gradient descent optimization algorithms, su ruder.io.

siam.org

epubs.siam.org

V. Patel, Kalman-Based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning, in SIAM Journal on Optimization, vol. 26, n. 4, 1º gennaio 2016, pp. 2620–2648, DOI:10.1137/15M1048239, ISSN 1052-6234 (WC · ACNP), arXiv:1512.01139.
D. Bertsekas, Incremental Least Squares Methods and the Extended Kalman Filter, in SIAM Journal on Optimization, vol. 6, n. 3, 1º agosto 1996, pp. 807–822, DOI:10.1137/S1052623494268522, ISSN 1052-6234 (WC · ACNP).

unibo.it

acnpsearch.unibo.it

Krzysztof C. Kiwiel, Convergence and efficiency of subgradient methods for quasiconvex minimization, in Mathematical Programming (Series A), vol. 90, n. 1, Berlin, Heidelberg, Springer, 2001, pp. 1–25, DOI:10.1007/PL00011414, ISSN 0025-5610 (WC · ACNP).
V. Patel, Kalman-Based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning, in SIAM Journal on Optimization, vol. 26, n. 4, 1º gennaio 2016, pp. 2620–2648, DOI:10.1137/15M1048239, ISSN 1052-6234 (WC · ACNP), arXiv:1512.01139.
D. Bertsekas, Incremental Least Squares Methods and the Extended Kalman Filter, in SIAM Journal on Optimization, vol. 6, n. 3, 1º agosto 1996, pp. 807–822, DOI:10.1137/S1052623494268522, ISSN 1052-6234 (WC · ACNP).

utoronto.ca

cs.utoronto.ca

Ilya Sutskever, Martens, James, Dahl, George e Hinton, Geoffrey E., On the importance of initialization and momentum in deep learning (PDF), a cura di Sanjoy Dasgupta and David Mcallester, In Proceedings of the 30th international conference on machine learning (ICML-13), vol. 28, Atlanta, GA, giugno 2013, pp. 1139–1147. URL consultato il 14 gennaio 2016.
Ilya Sutskever, Training recurrent neural networks (PDF) (Ph.D.), University of Toronto, 2013, p. 74.

web.archive.org

John Duchi, Elad Hazan e Yoram Singer, Adaptive subgradient methods for online learning and stochastic optimization (PDF), in JMLR, vol. 12, 2011, pp. 2121–2159. URL consultato il 10 giugno 2018 (archiviato dall'url originale il 28 maggio 2019).
Joseph Perla, Notes on AdaGrad (PDF), su seed.ucsd.edu, 2014 (archiviato dall'url originale il 30 marzo 2015).
Maya R. Gupta, Samy Bengio e Jason Weston, Training highly multiclass classifiers (PDF), in JMLR, vol. 15, n. 1, 2014, pp. 1461–1492. URL consultato il 10 giugno 2018 (archiviato dall'url originale il 25 ottobre 2018).
Geoffrey Hinton, Overview of mini-batch gradient descent (PDF), su cs.toronto.edu, pp. 27–29. URL consultato il 27 settembre 2016 (archiviato dall'url originale il 23 novembre 2016).

worldcat.org

Krzysztof C. Kiwiel, Convergence and efficiency of subgradient methods for quasiconvex minimization, in Mathematical Programming (Series A), vol. 90, n. 1, Berlin, Heidelberg, Springer, 2001, pp. 1–25, DOI:10.1007/PL00011414, ISSN 0025-5610 (WC · ACNP).
V. Patel, Kalman-Based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning, in SIAM Journal on Optimization, vol. 26, n. 4, 1º gennaio 2016, pp. 2620–2648, DOI:10.1137/15M1048239, ISSN 1052-6234 (WC · ACNP), arXiv:1512.01139.
D. Bertsekas, Incremental Least Squares Methods and the Extended Kalman Filter, in SIAM Journal on Optimization, vol. 6, n. 3, 1º agosto 1996, pp. 807–822, DOI:10.1137/S1052623494268522, ISSN 1052-6234 (WC · ACNP).