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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
Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data
Mohammad Azizmalayeri, Ameen Abu-Hanna, Giovanni Ciná
Despite their success, Machine Learning (ML) models do not generalize effectively to data not originating from the training distribution. To reliably employ ML models in real-world…