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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.LG2026
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…