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 431–440 of 3073 papers

TitleStatusHype
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Active Learning: Problem Settings and Recent Developments—0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—0
Active Learning with Expert Advice—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Active Learning for Deep Neural Networks on Edge Devices—0
Active Learning with Importance Sampling—0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
Active Learning for Community Detection in Stochastic Block Models—0
Active Deep Learning on Entity Resolution by Risk Sampling—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