= Chuan Guo
{c}
<Chuan Guo> is a <computer scientist> and coauthor of <On Calibration of Modern Neural Networks by Chuan Guo and others> (2017), which studies whether neural-network confidence agrees with observed correctness. Its experiments and post-processing comparisons helped make <probability calibration> a concrete evaluation question alongside classification accuracy. The paper marks its first three authors as equal contributors; the work should not be attributed solely to the first-listed author.<On Calibration of Modern Neural Networks by Chuan Guo and others>[First-page byline, equal-contribution note and abstract]{ref}
The distinction can be illustrated without reproducing an experiment. Suppose a synthetic classifier makes $100$ predictions, of which $60$ are correct, and reports confidence $0.99$ for every selected class. Its accuracy is $60\%$, while its mean confidence is $99\%$. Changing only the reported confidence to $0.60$ leaves the predictions and accuracy unchanged but removes this group's mean confidence gap. Aggregate agreement still does not prove calibration within every subgroup.
<Temperature scaling>, examined in the paper, makes a related distinction operational by fitting one positive scalar to adjust probabilities while preserving the selected class within each example. The mathematical transformation and its measured limitations belong to <temperature scaling>; they do not imply that every classifier can be made perfectly calibrated with one number.<On Calibration of Modern Neural Networks by Chuan Guo and others>[§4.2, Eq. (9); §5, calibration comparisons]{ref}
Homepage: https://sites.google.com/view/chuanguo . The page lists the calibration paper, distinguishing this author from other researchers with the same name.
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