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 1071–1080 of 3073 papers

TitleStatusHype
BAOD: Budget-Aware Object Detection—0
Active Learning with Rationales for Text Classification—0
Active Learning for Dependency Parsing with Partial Annotation—0
Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise—0
Correlation Clustering with Active Learning of Pairwise Similarities—0
Active Learning for Dependency Parsing by A Committee of Parsers—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
ActDroid: An active learning framework for Android malware detection—0
A Benchmark and Comparison of Active Learning for Logistic Regression—0
Active Learning for WBAN-based Health Monitoring—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