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 901–910 of 3073 papers

TitleStatusHype
AI For Fraud Awareness—0
Active Learning for Dependency Parsing with Partial Annotation—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—0
Batch Active Learning of Reward Functions from Human Preferences—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Batch Active Learning via Coordinated Matching—0
Active Learning with Simple Questions—0
Active learning for detection of stance components—0
Active Learning with Statistical Models—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—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