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 2401–2410 of 3073 papers

TitleStatusHype
Active Learning for Skewed Data Sets—0
Active Learning for Sound Event Detection—0
Active Learning for Speech Recognition: the Power of Gradients—0
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models—0
Active Learning for Structured Prediction from Partially Labeled Data—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
Active Learning for Saddle Point Calculation—0
Active Learning for Undirected Graphical Model Selection—0
Active Learning for Video Classification with Frame Level Queries—0
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking—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