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 2571–2580 of 3073 papers

TitleStatusHype
Active Stacking for Heart Rate Estimation—0
Active Test-time Vision-Language Navigation—0
Active Third-Person Imitation Learning—0
Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations—0
Active Transfer Learning for Persian Offline Signature Verification—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks—0
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data—0
Active Universal Domain Adaptation—0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation—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