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

Showing 21–30 of 3073 papers

TitleStatusHype
Active Prompting with Chain-of-Thought for Large Language ModelsCode2
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMsCode2
DCoM: Active Learning for All LearnersCode2
Transductive Active Learning: Theory and ApplicationsCode2
Generative Active Learning for Long-tailed Instance SegmentationCode2
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical ImagesCode2
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three WeeksCode2
Physics-informed active learning for accelerating quantum chemical simulationsCode2
Sailing AI by the Stars: A Survey of Learning from Rewards in Post-Training and Test-Time Scaling of Large Language ModelsCode2
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
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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