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Optimal Synthesis of IDK-Cascades

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Title of host publicationRTNS'2021: 29th International Conference on Real-Time Networks and Systems
DateAccepted/In press - 1 Apr 2021
DatePublished (current) - 22 Jul 2021
Pages184-191
Number of pages8
Original languageEnglish

Abstract

A classifier is a software component, often based upon deep learning (DL), that categorizes each input provided to it into one of a fixed set of classes. An IDK classifier may additionally output an 'I don't know' (IDK) on certain input. Given several different IDK classifiers for the same operation, the problem is considered of using them in concert in such a manner that the average duration to successfully classify any input is minimized. Optimal algorithms are proposed for solving this problem, both as is and under an additional constraint that the operation must be completed within a specified hard deadline.

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© 2021 Association for Computing Machinery. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

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