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 1951–1960 of 3073 papers

TitleStatusHype
Stopping Active Learning based on Predicted Change of F Measure for Text Classification—0
Stopping criterion for active learning based on deterministic generalization bounds—0
Strat\'egies de s\'election des exemples pour l'apprentissage actif avec des champs al\'eatoires conditionnels—0
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss—0
Stream-Based Active Learning for Process Monitoring—0
Stream-based active learning with linear models—0
Stream-based Online Active Learning in a Contextual Multi-Armed Bandit Framework—0
Streaming Active Deep Forest for Evolving Data Stream Classification—0
Streaming Active Learning for Regression Problems Using Regression via Classification—0
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization—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