SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2331–2340 of 3073 papers

TitleStatusHype
Active Learning for Delineation of Curvilinear Structures—0
Active Learning for Dependency Parsing by A Committee of Parsers—0
Active Learning for Dependency Parsing with Partial Annotation—0
Active learning for detection of stance components—0
Active Learning for Direct Preference Optimization—0
Active learning for distributionally robust level-set estimation—0
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant—0
Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation—0
Active learning for efficient data selection in radio-signal based positioning via deep learning—0
Active Learning for Efficient Testing of Student Programs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified