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 2321–2330 of 3073 papers

TitleStatusHype
Active Learning for Control-Oriented Identification of Nonlinear Systems—0
Active Learning for Coreference Resolution—0
Active Learning for Coreference Resolution—0
Active Learning for Cost-Sensitive Classification—0
Active Learning for Crowd-Sourced Databases—0
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation—0
Active Learning for Deep Neural Networks on Edge Devices—0
Active Learning for Deep Object Detection—0
Active learning for deep semantic parsing—0
Active Learning for Deep Visual Tracking—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