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Found 5 papers, 2 papers with code
Out-Of-Distribution Detection Is Not All You Need
In this work, we argue that OOD detection is not a well-suited framework to design efficient runtime monitors and that it is more relevant to evaluate monitors based on their ability to discard incorrect predictions.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Unifying Evaluation of Machine Learning Safety Monitors
This paper introduces three unified safety-oriented metrics, representing the safety benefits of the monitor (Safety Gain), the remaining safety gaps after using it (Residual Hazard), and its negative impact on the system's performance (Availability Cost).
Evaluation of Runtime Monitoring for UAV Emergency Landing
The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research.
Certifying Emergency Landing for Safe Urban UAV
Unmanned Aerial Vehicles (UAVs) have the potential to be used for many applications in urban environments.
White Paper Machine Learning in Certified Systems
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc.
