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 1831–1840 of 3073 papers

TitleStatusHype
Robust Active Distillation—0
Robust Active Learning for Electrocardiographic Signal Classification—0
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation—0
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models—0
Robust Active Learning Strategies for Model Variability—0
Robust Adaptive Submodular Maximization—0
Robust and Active Learning for Deep Neural Network Regression—0
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion—0
Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations—0
Robust expected improvement for Bayesian optimization—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