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• W3157189172 abstract "We present geometric Bayesian active learning by disagreements (GBALD), a framework that performs BALD on its core-set construction interacting with model uncertainty estimation. Technically, GBALD constructs core-set on ellipsoid, not typical sphere, preventing low-representative elements from spherical boundaries. The improvements are twofold: 1) relieve uninformative prior and 2) reduce redundant estimations. Theoretically, geodesic search with ellipsoid can derive tighter lower bound on error and easier to achieve zero error than with sphere. Experiments show that GBALD has slight perturbations to noisy and repeated samples, and outperforms BALD, BatchBALD and other existing deep active learning approaches." @default.
• W3157189172 created "2021-05-10" @default.
• W3157189172 creator A5021751767 @default.
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• W3157189172 date "2021-05-06" @default.
• W3157189172 modified "2023-09-27" @default.
• W3157189172 title "Bayesian Active Learning by Disagreements: A Geometric Perspective." @default.
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