Predicted Robustness as QoS for Deep Neural Network Models-Reference-Cited by-同舟云学术

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Predicted Robustness as QoS for Deep Neural Network Models

Published:2020-09-30 Issue:5 Volume:35 Page:999-1015
ISSN:1000-9000
Container-title:Journal of Computer Science and Technology
language:en
Short-container-title:J. Comput. Sci. Technol.

Author:

Wang Yue-Huan,Li Ze-Nan,Xu Jing-Wei,Yu Ping,Chen Taolue,Ma Xiao-Xing

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Computer Science Applications,Hardware and Architecture,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11390-020-0482-6.pdf

Reference48 articles.

1. Andor D, Alberti C, Weiss D, Severyn A, Presta A, Ganchev K, Petrov S, Collins M. Globally normalized transition-based neural networks. arXiv:1603.06042, 2016. https://arxiv.org/abs/1603.06042, June 2020.

2. Hinton G, Deng L, Yu D, Dahl G, Mohamed A, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Kingsbury B. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 2012, 29(6): 82-97.

3. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proc. the IEEE Conference on Computer Vision and Pattern Recognition, June 2016, pp.770-778.

4. Wang X, Huang C, Yao L, Benatallah B, Dong M. A survey on expert recommendation in community question answering. Journal of Computer Science and Technology, 2018, 33(4): 625-653.

5. Liu Q, Zhao H K, Wu L, Li Z, Chen E H. Illuminating recommendation by understanding the explicit item relations. Journal of Computer Science and Technology, 2018, 33(4): 739-755.

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