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 1821–1830 of 3073 papers

TitleStatusHype
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification—0
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification—0
Best Practices in Active Learning for Semantic Segmentation—0
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning—0
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces—0
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs—0
Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition—0
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification—0
Robot Design With Neural Networks, MILP Solvers and Active Learning—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