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 2411–2420 of 3073 papers

TitleStatusHype
Active Learning for Vision-Language Models—0
Active Learning for Wireless IoT Intrusion Detection—0
Active Learning Framework to Automate NetworkTraffic Classification—0
Active Learning from Crowd in Document Screening—0
Active Learning from Imperfect Labelers—0
Active Learning from Peers—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
Active Learning from Weak and Strong Labelers—0
Active Learning Graph Neural Networks via Node Feature Propagation—0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation—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