Detailed Notes on A-028-45
Detailed Notes on A-028-45
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Look at PDF Summary:Discriminative neural networks offer you little if any functionality guarantees when deployed on knowledge not created by the identical system since the schooling distribution. On this sort of out-of-distribution (OOD) inputs, the prediction may perhaps not just be faulty, but confidently so, restricting the Protected deployment of classifiers in serious-globe apps. One this sort of hard application is microorganisms identification based upon genomic sequences, which holds the promise of early detection of disorders, but demands a model that will output very low confidence predictions on OOD genomic sequences from new microorganisms which were not existing in the coaching knowledge. We introduce a genomics dataset for OOD detection that enables other researchers to benchmark progress on this critical trouble.
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Heterogeneity in methodology and reporting prevented meta-Examination and we therefore offer a narrative synthesis.
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