1. Building Robust Machine Learning Systems: Current Progress, Research Challenges, and Opportunities
- Author
-
Zhang, Jeff Jun, Liu, Kang, Khalid, Faiq, Hanif, Muhammad Abdullah, Rehman, Semeen, Theocharides, Theocharis, Artussi, Alessandro, Shafique, Muhammad, Garg, Siddharth, and Theocharides, Theocharis [0000-0001-7222-9152]
- Subjects
010302 applied physics ,Computer science ,business.industry ,Deep learning ,02 engineering and technology ,Research opportunities ,Machine learning ,computer.software_genre ,01 natural sciences ,020202 computer hardware & architecture ,Robustness (computer science) ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,Artificial intelligence ,business ,computer - Abstract
Machine learning, in particular deep learning, is being used in almost all the aspects of life to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications. Due to its state-of-the-art performance, deep learning is also being employed in safety-critical applications, for instance, autonomous vehicles. Reliability and security are two of the key required characteristics for these applications because of the impact they can have on human's life. Towards this, in this paper, we highlight the current progress, challenges and research opportunities in the domain of robust systems for machine learning-based applications. 1 4
- Published
- 2019