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 1391–1400 of 3073 papers

TitleStatusHype
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
Active Learning in Noisy Conditions for Spoken Language Understanding—0
Domain Adaptation with Active Learning for Coreference Resolution—0
Domain Adversarial Active Learning for Domain Generalization Classification—0
Distribution-Dependent Sample Complexity of Large Margin Learning—0
An Active Learning Framework for Efficient Robust Policy Search—0
Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media—0
Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers—0
Distribution Aware Active Learning—0
Distributional Term Set Expansion—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