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 491–500 of 3073 papers

TitleStatusHype
Domain Adversarial Active Learning for Domain Generalization Classification—0
Active Generalized Category DiscoveryCode2
On the Topology Awareness and Generalization Performance of Graph Neural Networks—0
Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine—0
SUPClust: Active Learning at the Boundaries—0
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
Active Statistical InferenceCode1
Active Learning of Mealy Machines with Timers—0
Improving Uncertainty Sampling with Bell Curve Weight Function—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