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 1631–1640 of 3073 papers

TitleStatusHype
On the use of uncertainty in classifying Aedes Albopictus mosquitoes—0
Convergence of Uncertainty Sampling for Active Learning—0
Teaching an Active Learner with Contrastive Examples—0
RIM: Reliable Influence-based Active Learning on GraphsCode0
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising TreesCode0
Failure-averse Active Learning for Physics-constrained Systems—0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
Single-Modal Entropy based Active Learning for Visual Question Answering—0
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model BiasCode0
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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