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Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world.
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James MacQueen et al · 1967
Earlier work this paper cites.
Wordnet: a lexical database for english
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Earlier work this paper cites.
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Earlier work this paper cites.
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