Abstract
Recently, if the behavior of hidden units is regularized during learning. For example, by randomly dropping 50% of their activities, it has been shown that neural network performs very well. We define a process called “dropout,” in which connections and neurons from the neural network are dropped. Together with the neural network, this dropout neural network can be trained by roughly computing local expectations of dropout’s variables at connections using backpropagation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Labach, A., Salehinejad, H., Valaee, S.: Survey of Dropouts Methods for Deep Neural Networks. arXiv:1904:13310v1 (2019)
Matsuoka, K.: Noise injection into inputs in back-propagation learning. IEEE Trans. Syst. Man Cybern. 22(3), 436–440 (1992)
Baldi, P., Sadowski, P.: Understanding Dropouts. University of California
Srivastava, N., Hinton, G., Krizhensky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural network from overfitting. J. Mach. Learn. Res. (JMLR) (2014)
Phaisangittiasagul, E.: An analysis of the regularization between L2 and dropout in the single hidden layer neural network. In: International Conference on Intelligent Systems, Modelling and Simulation (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kausar, F., Aishwarya, P., Shyam, G.K. (2022). Fault Tolerance Analysis in Neural Networks Using Dropouts. In: Dua, M., Jain, A.K., Yadav, A., Kumar, N., Siarry, P. (eds) Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-5747-4_62
Download citation
DOI: https://doi.org/10.1007/978-981-16-5747-4_62
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5746-7
Online ISBN: 978-981-16-5747-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)