Design methodology for deep out-of-distribution detectors in real-time cyber-physical systems

Design methodology for deep out-of-distribution detectors in real-time cyber-physical systems

When machine learning (ML) models are supplied with data outside their training distribution, they are more likely to make inaccurate predictions; in a cyber-physical system (CPS), this could lead to catastrophic system failure. To mitigate this risk, an out-of-distribution (OOD) detector can run in...

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Bibliographic Details
Main Authors: Yuhas, Michael, Ng, Daniel Jun Xian, Easwaran, Arvind
Other Authors: College of Computing and Data Science
Format: Conference Paper
Language:English
Published: 2024
Subjects:
Computer and Information Science
Quantization (signal)
Design methodology
Online Access:https://hdl.handle.net/10356/178682

Internet

https://hdl.handle.net/10356/178682

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