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cs.LG2026
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Kevin Wilkinghoff, Neelu Madan, Juan Miguel Valverde +6
Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are trained exclusively on norm…
cs.LG2024
Verifying Machine Unlearning with Explainable AI
Àlex Pujol Vidal, Anders S. Johansen, Mohammad N. S. Jahromi +3
We investigate the effectiveness of Explainable AI (XAI) in verifying Machine Unlearning (MU) within the context of harbor front monitoring, focusing on data privacy and regulatory…