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- W3019573762 abstract "Small, labelled datasets in the presence of larger, unlabelled datasets pose challenges to data-hungry deep learning algorithms. Such scenarios are prevalent in healthcare where labelling is expensive, time-consuming, and requires expert medical professionals. To tackle this challenge, we propose a family of active learning methodologies and acquisition functions dependent upon input and parameter perturbations which we call Active Learning via Perturbations (ALPS). We test our methods on six diverse time-series and image datasets and illustrate their benefit in the presence and absence of an oracle. We also show that acquisition functions that incorporate temporal information have the potential to predict the ability of networks to generalize." @default.
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- W3019573762 date "2020-04-20" @default.
- W3019573762 modified "2023-09-27" @default.
- W3019573762 title "ALPS: Active Learning via Perturbations" @default.
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