1 citations · 1 across the 4 of their papers we have counts for
5 papers
An Explainable GNN Framework for Component-Level Anomaly Diagnosis
Sena Ozgunay, Louise Travé-Massuyès, Louise Trav{é}-Massuy{è}s +2
Industrial processes are complex systems composed of multiple interacting sensors that generate multivariate time series (MTS). Detecting anomalies in such systems is critical for…
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…
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…
Benchmarking Safety Monitors for Image Classifiers with Machine Learning
Raul Sena Ferreira, Jean Arlat, Jeremie Guiochet +1
High-accurate machine learning (ML) image classifiers cannot guarantee that they will not fail at operation. Thus, their deployment in safety-critical applications such as autonomo…