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 2551–2560 of 3073 papers

TitleStatusHype
Active Regression by Stratification—0
Active Regression via Linear-Sample Sparsification—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings—0
Active Reinforcement Learning Strategies for Offline Policy Improvement—0
Active Relation Discovery: Towards General and Label-aware OpenRE—0
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
Active Robust Learning—0
Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking—0
Active Scene 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