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 651–660 of 3073 papers

TitleStatusHype
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data—0
ALICE: Active Learning with Contrastive Natural Language Explanations—0
Active Learning Framework to Automate NetworkTraffic Classification—0
Active Learning from Imperfect Labelers—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Active Learning from Peers—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
ALLSH: Active Learning Guided by Local Sensitivity and Hardness—0
ALLWAS: Active Learning on Language models in WASserstein space—0
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner—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