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 2501–2510 of 3073 papers

TitleStatusHype
Active Learning with Importance Sampling—0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
Active Learning with Label Comparisons—0
Active Learning with Logged Data—0
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation—0
Active Learning with Multiple Kernels—0
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
Active Learning with Oracle Epiphany—0
Correlation Clustering with Active Learning of Pairwise Similarities—0
Active Learning with Rationales for Text Classification—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