Robustness Attributes to Safeguard Machine Learning Models in Production
Author:
Affiliation:
1. Applied Artificial Intelligence Institute, Deakin University,VIC,Australia
2. Monash University,Department of Software Systems & Cybersecurity,VIC,Australia
3. STEM - School of Engineering, RMIT University,VIC,Australia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10520249/10520342/10520555.pdf?arnumber=10520555
Reference99 articles.
1. Secure and Robust Machine Learning for Healthcare: A Survey
2. RGB-D Fall Detection via Deep Residual Convolutional LSTM Networks
3. End to end learning for self-driving cars;Bojarski;arXiv preprint,2016
4. DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
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