3 papers
cs.LG2026
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…
cs.LG2024
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…
cs.LG2024
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…