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 1871–1880 of 3073 papers

TitleStatusHype
An Information-Theoretic Framework for Unifying Active Learning ProblemsCode0
GLISTER: Generalization based Data Subset Selection for Efficient and Robust LearningCode1
Rebuilding Trust in Active Learning with Actionable Metrics—0
Minimax Active Learning—0
Embodied Visual Active Learning for Semantic Segmentation—0
Learning active learning at the crossroads? evaluation and discussion—0
Active Learning for Deep Gaussian Process SurrogatesCode0
Chernoff Sampling for Active Testing and Extension to Active Regression—0
Deep Bayesian Active Learning, A Brief Survey on Recent Advances—0
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert LinkingCode1
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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