Real-Time Machine Learning for Quickest DetectionSpeaker: Houbing Song – Baltimore, MD, United States
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
AbstractQuickest detection, which refers to real-time detection of abrupt changes in the behavior of an observed signal or time series as quickly as possible after they occur, is essential to enable safety, security, and dependability of cyber-physical systems (CPS). Real-Time Machine Learning (RTML) has the potential to achieve quickest detection. However, Machine learning lacks the necessary mathematical framework to provide guarantees on correctness. The integration of machine learning with quickest detection not only creates new research opportunities with major societal implications, but also poses new research challenges in safety, security, and dependability. In this lecture, I will present a comprehensive survey of existing literature in the emerging area of real-time machine learning for quickest detection, identify the challenges, and evaluate the trends. I will also introduce our research findings in this area.
About this LectureNumber of Slides: 45
Duration: 60 minutes
Languages Available: English
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