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 1101–1110 of 3073 papers

TitleStatusHype
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
Adaptive Selective Sampling for Online Prediction with Experts—0
Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties—0
Active Learning for Online Recognition of Human Activities from Streaming Videos—0
Adaptive Region-Based Active Learning—0
Active Learning for One-Class Classification Using Two One-Class Classifiers—0
Convergence of Uncertainty Sampling for Active Learning—0
Convergence Rates of Active Learning for Maximum Likelihood Estimation—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
Active Learning for Object Detection with Non-Redundant Informative Sampling—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