Hyperspherical Pooled Mahalanobis (HPM) normalizes frozen long-tailed features to the unit sphere and uses pooled ridge-regularized covariance to improve raw Mahalanobis OOD scoring, lifting AUROC from 46.49 to 85.67 on CIFAR-10-LT.
International Conference on Learning Representations , year=
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Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.
citing papers explorer
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Is Complex Training Necessary for Long-Tailed OOD Detection? A Re-think from Feature Geometry
Hyperspherical Pooled Mahalanobis (HPM) normalizes frozen long-tailed features to the unit sphere and uses pooled ridge-regularized covariance to improve raw Mahalanobis OOD scoring, lifting AUROC from 46.49 to 85.67 on CIFAR-10-LT.
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Language Models (Mostly) Know What They Know
Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.