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 1841–1850 of 3073 papers

TitleStatusHype
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification—0
Robust online active learning—0
Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow—0
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data—0
Role-Playing Simulation Games using ChatGPT—0
RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure—0
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian—0
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification—0
SABAL: Sparse Approximation-based Batch Active Learning—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