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 981–990 of 3073 papers

TitleStatusHype
Active operator learning with predictive uncertainty quantification for partial differential equations—0
Big Batch Bayesian Active Learning by Considering Predictive Probabilities—0
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis—0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora—0
Bilingual Transfer Learning for Online Product Classification—0
Agnostic Active Learning Without Constraints—0
Active Learning for Speech Recognition: the Power of Gradients—0
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers—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