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 581–590 of 3073 papers

TitleStatusHype
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy—0
Active Learning of Abstract Plan Feasibility—0
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation—0
Active Crowd Counting with Limited Supervision—0
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval—0
Active Learning Methods based on Statistical Leverage Scores—0
Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems—0
Active learning machine learns to create new quantum experiments—0
Active Covering—0
Active covariance estimation by random sub-sampling of variables—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