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 951–960 of 3073 papers

TitleStatusHype
Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing—0
Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize—0
Data efficient deep learning for medical image analysis: A survey—0
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource LearnersCode0
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation—0
Guideline Learning for In-context Information Extraction—0
Improved Active Learning via Dependent Leverage Score Sampling—0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy—0
Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual RepresentationCode0
Pool-Based Active Learning with Proper Topological Regions—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