Web: http://arxiv.org/abs/2210.01964

Oct. 7, 2022, 1:13 a.m. | A. Michael Carrell, Neil Mallinar, James Lucas, Preetum Nakkiran

cs.LG updates on arXiv.org arxiv.org

Calibration is a fundamental property of a good predictive model: it requires
that the model predicts correctly in proportion to its confidence. Modern
neural networks, however, provide no strong guarantees on their calibration --
and can be either poorly calibrated or well-calibrated depending on the
setting. It is currently unclear which factors contribute to good calibration
(architecture, data augmentation, overparameterization, etc), though various
claims exist in the literature.


We propose a systematic way to study the calibration error: by decomposing …

arxiv gap

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