MATEC Web Conf.
Volume 232, 20182018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|Number of page(s)||4|
|Section||Algorithm Study and Mathematical Application|
|Published online||19 November 2018|
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, pp. 1097–1105, 2012. [Google Scholar]
- Robbins, Herbert and Monro, Sutton. A stochastic approximation method. The annals of mathematical statistics,pp. 400–407, 1951. [CrossRef] [MathSciNet] [Google Scholar]
- Kingma, D. and Ba, J. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR 2015), 2015 [Google Scholar]
- Duchi, J., Hazan, E., and Singer, Y. Adaptive subgradient methods for online learning and stochastic optimization.The Journal of Machine Learning Research, 12:2121–2159, 2011. [Google Scholar]
- Tieleman, T. and Hinton, G. Lecture 6.5-RMSProp: Divide the gradient by a running average of its recent magni-tude. COURSERA:Neural Networks for Machine Learning, 4, 2012. [Google Scholar]
- Nitish Shirish Keskar, Richard Socher . Improving Generalization Performance by Switching from Adam to SGD. In International conference on machine learning, pp. 200–210, 2017. [Google Scholar]
- Wilson, A. C., Roelofs, R., Stern, M., Srebro, N., and Recht, B. The Marginal Value of Adaptive Gradient Methods in Machine Learning. ArXiv e-prints, May 2017. [Google Scholar]
- Sashank J. Reddi, Satyen Kale & Sanjiv Kumar. On the Convergence of Adam and beyond. Published as a conference paper at ICLR 2018, 2018. [Google Scholar]
- Sutskever, I., Martens, J., Dahl, G., and Hinton, G. On the importance of initialization and momentum in deeplearning. In International conference on machine learning, pp. 1139–1147,2013. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.