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 821–830 of 3073 papers

TitleStatusHype
Autonomous synthesis of metastable materials—0
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography—0
A Survey of Latent Factor Models in Recommender Systems—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis—0
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification—0
Bayesian Active Learning for Structured Output Design—0
Active Learning under Label Shift—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
ALARM: Active LeArning of Rowhammer Mitigations—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