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 31–40 of 3073 papers

TitleStatusHype
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Active Learning Meets Optimized Item SelectionCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
Active Learning for Open-set AnnotationCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
Active Imitation Learning with Noisy GuidanceCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
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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