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 1291–1300 of 3073 papers

TitleStatusHype
Active Learning with Tabular Language Models—0
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes—0
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler—0
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Neural Active Learning on Heteroskedastic DistributionsCode0
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop—0
Batch Active Learning from the Perspective of Sparse Approximation—0
Oracle-guided Contrastive Clustering—0
Entity Matching by Pool-based Active Learning—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