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 2291–2300 of 3073 papers

TitleStatusHype
Active-learning-based non-intrusive Model Order Reduction—0
Active Learning based on Data Uncertainty and Model Sensitivity—0
Active Learning-Based Optimization of Scientific Experimental Design—0
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage—0
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques—0
Active Learning by Query by Committee with Robust Divergences—0
Active Learning by Querying Informative and Representative Examples—0
Active Learning Classification from a Signal Separation Perspective—0
Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems—0
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation—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