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 2131–2140 of 3073 papers

TitleStatusHype
Heuristic Stopping Rules For Technology-Assisted Review—0
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance—0
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights—0
Hierarchical Optimistic Region Selection driven by Curiosity—0
Hierarchical Subquery Evaluation for Active Learning on a Graph—0
Hierarchical Uncertainty Aggregation and Emphasis Loss for Active Learning in Object Detection—0
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation—0
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks—0
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex—0
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users—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