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 1501–1510 of 3073 papers

TitleStatusHype
Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint—0
Episode-Based Active Learning with Bayesian Neural Networks—0
Epistemic Uncertainty Quantification For Pre-trained Neural Network—0
Epistemic Uncertainty Quantification For Pre-Trained Neural Networks—0
Epistemic Uncertainty Sampling—0
Error-Tolerant Exact Query Learning of Finite Set Partitions with Same-Cluster Oracle—0
Fair Active Learning: Solving the Labeling Problem in Insurance—0
Efficient Epistemic Uncertainty Estimation in Regression Ensemble Models Using Pairwise-Distance Estimators—0
Estimating Optimal Active Learning via Model Retraining Improvement—0
Active Universal Domain Adaptation—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