Web: http://arxiv.org/abs/2206.11562

June 24, 2022, 1:11 a.m. | Gabriella Chouraqui, Liron Cohen, Gil Einziger, Liel Leman

stat.ML updates on arXiv.org arxiv.org

Machine learning classifiers are probabilistic in nature, and thus inevitably
involve uncertainty. Predicting the probability of a specific input to be
correct is called uncertainty (or confidence) estimation and is crucial for
risk management. Post-hoc model calibrations can improve models' uncertainty
estimations without the need for retraining, and without changing the model.
Our work puts forward a geometric-based approach for uncertainty estimation.
Roughly speaking, we use the geometric distance of the current input from the
existing training inputs as a …

arxiv lg real-time time uncertainty

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