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 1641–1650 of 3073 papers

TitleStatusHype
Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users—0
Tuning Deep Active Learning for Semantic Role Labeling—0
Turning silver into gold: error-focused corpus reannotation with active learning—0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
Two Stream Active Query Suggestion for Active Learning in Connectomics—0
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction—0
U-GIFT: Uncertainty-Guided Firewall for Toxic Speech in Few-Shot Scenario—0
Ulcerative Colitis Mayo Endoscopic Scoring Classification with Active Learning and Generative Data Augmentation—0
Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach—0
UMat: Uncertainty-Aware Single Image High Resolution Material Capture—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