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 2421–2430 of 3073 papers

TitleStatusHype
Generalized active learning and design of statistical experiments for manifold-valued data—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Empirical Evaluations of Active Learning Strategies in Legal Document Review—0
Active Learning for Network Intrusion Detection—0
Sequential Adaptive Design for Jump Regression Estimation—0
An Atomistic Machine Learning Package for Surface Science and CatalysisCode0
Active Multi-Information Source Bayesian Quadrature—0
Active Stacking for Heart Rate Estimation—0
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification—0
Active Learning of Spin Network ModelsCode0
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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