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Watch PDF Abstract:Discriminative neural networks present little or no functionality guarantees when deployed on knowledge not created by the identical system since the schooling distribution. On these kinds of out-of-distribution (OOD) inputs, the prediction may not merely be faulty, but confidently so, limiting the Protected deployment of classifiers in serious-globe apps. One these kinds of complicated application is bacteria identification according to genomic sequences, which retains the promise of early detection of conditions, but demands a model that will output lower confidence predictions on OOD genomic sequences from new germs that were not current in the instruction facts. We introduce a genomics dataset for OOD detection which allows other researchers to benchmark progress on this crucial challenge.

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