1 citations · 1 across the 5 of their papers we have counts for
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Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing
Mathieu Dario, Florent Chenevier, Kévin Delmas +2
Runtime monitoring is essential to ensure the safety of ML applications in safety-critical domains. However, current research is fragmented, with independent methods emerging from…
Safety Monitoring of Machine Learning Perception Functions: a Survey
Raul Sena Ferreira, Joris Guérin, Kevin Delmas +2
Machine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks. New dependability challenges arise when ML…
Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring
Khoi Tran Dang, Kevin Delmas, Jérémie Guiochet +1
With the increasing use of neural networks in critical systems, runtime monitoring becomes essential to reject unsafe predictions during inference. Various techniques have emerged…
Out-Of-Distribution Detection Is Not All You Need
Joris Guérin, Kevin Delmas, Raul Sena Ferreira +1
The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee their correct behavior. Runtime monitors are components aiming to identify unsaf…
Unifying Evaluation of Machine Learning Safety Monitors
Joris Guerin, Raul Sena Ferreira, Kevin Delmas +1
With the increasing use of Machine Learning (ML) in critical autonomous systems, runtime monitors have been developed to detect prediction errors and keep the system in a safe stat…