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 361–370 of 3073 papers

TitleStatusHype
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant—0
ActDroid: An active learning framework for Android malware detection—0
Active learning for distributionally robust level-set estimation—0
Active Learning for Direct Preference Optimization—0
Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification—0
Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis—0
Active learning for detection of stance components—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Active Learning for WBAN-based Health Monitoring—0
Active Learning for Dependency Parsing with Partial Annotation—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