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 451–460 of 3073 papers

TitleStatusHype
Active Learning with Safety Constraints—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Active Learning with Simple Questions—0
Active Learning with Statistical Models—0
Active Learning with Tabular Language Models—0
Active Learning for Direct Preference Optimization—0
Active Learning with TensorBoard Projector—0
Active Learning with Transfer Learning—0
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys—0
Active Learning Over Multiple Domains in Natural Language Tasks—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