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 2971–2980 of 3073 papers

TitleStatusHype
Near Optimal Bayesian Active Learning for Decision Making—0
Selective Sampling with Drift—0
Human Activity Recognition using Smartphone—0
Toward Supervised Anomaly Detection—0
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data—0
Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff—0
Active Discovery of Network Roles for Predicting the Classes of Network Nodes—0
Active Player Modelling—0
Latent Structured Active Learning—0
Statistical Active Learning Algorithms—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