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cs.LG2024
Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations
Mohammad Azizmalayeri, Ameen Abu-Hanna, Giovanni Cinà
Detecting out-of-distribution (OOD) instances is crucial for the reliable deployment of machine learning models in real-world scenarios. OOD inputs are commonly expected to cause a…
cs.LG2023
Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
Izak Yasrebi-de Kom, Joanna Klopotowska, Dave Dongelmans +4
The current best practice approach for the retrospective diagnosis of adverse drug events (ADEs) in hospitalized patients relies on a full patient chart review and a formal causali…