Name
Title | ||
|---|---|---|
Building Robust Machine Learning Systems: Current Progress, Research Challenges, and Opportunities |
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.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3316781.3323472 | Proceedings of the 56th Annual Design Automation Conference 2019 |
Keywords | DocType | ISBN |
Adversarial Attacks, Deep Learning, Machine Learning, Permanent Faults, Reliability, Robustness, Security, Timing Errors | Conference | 978-1-4503-6725-7 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeff Jun Zhang | 1 | 12 | 1.90 |
| Kang Liu | 2 | 52 | 7.60 |
| Faiq Khalid Lodhi | 3 | 54 | 10.33 |
| Muhammad Abdullah Hanif | 4 | 71 | 18.12 |
| Semeen Rehman | 5 | 447 | 31.92 |
| Theo Theocharides | 6 | 7 | 2.75 |
| Alessandro Artussi | 7 | 2 | 0.36 |
| Muhammad Shafique | 8 | 1945 | 157.67 |
| Siddharth Garg | 9 | 675 | 55.14 |