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 1161–1170 of 3073 papers

TitleStatusHype
Addressing Limited Data for Textual Entailment Across Domains—0
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning—0
Active Learning for Non-Parametric Choice Models—0
Active Learning for Post-Editing Based Incrementally Retrained MT—0
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost—0
Cost-effective Variational Active Entity Resolution—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—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