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 2131–2140 of 3073 papers

TitleStatusHype
Active Learning for Skewed Data Sets—0
Batch Decorrelation for Active Metric LearningCode0
Stopping criterion for active learning based on deterministic generalization bounds—0
VirAAL: Virtual Adversarial Active Learning For NLUCode0
Empowering Active Learning to Jointly Optimize System and User DemandsCode0
Deeply Supervised Active Learning for Finger Bones SegmentationCode0
Active Learning with Multiple Kernels—0
Modeling nanoconfinement effects using active learning—0
Localized active learning of Gaussian process state space models—0
Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations—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