June 6, 2024, 4:45 a.m. | Kajsa M{\o}llersen, Einar Holsb{\o}

cs.LG updates on arXiv.org arxiv.org

arXiv:2303.07272v4 Announce Type: replace-cross
Abstract: Machine learning methods are commonly evaluated and compared by their performance on data sets from public repositories. This allows for multiple methods, oftentimes several thousands, to be evaluated under identical conditions and across time. The highest ranked performance on a problem is referred to as state-of-the-art (SOTA) performance, and is used, among other things, as a reference point for publication of new methods. Using the highest-ranked performance as an estimate for SOTA is a biased …

abstract accounting art arxiv benchmark cs.lg data data sets machine machine learning multiple performance problem public replace repositories sota state stat.me type

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