69 citations · 144 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
Spatially-constrained clustering of geospatial features for heat vulnerability assessment of favelas in Rio de Janeiro
Baptiste Clemence, Thomas Hallopeau, Vanderlei Pascoal De Matos +2
Informal settlements face disproportionate exposure to climate-related health hazards. However, existing methodologies lack systematic approaches to link diverse settlement charact…
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…
Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm
Joris Guérin, Olivier Gibaru, Stéphane Thiery +1
This paper describes a method for clustering data that are spread out over large regions and which dimensions are on different scales of measurement. Such an algorithm was develope…