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

Showing 171180 of 3073 papers

TitleStatusHype
Active Learning by Acquiring Contrastive ExamplesCode1
Active Learning by Feature MixingCode1
Can Active Learning Preemptively Mitigate Fairness Issues?Code1
Cartography Active LearningCode1
Active Learning from the WebCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Class-Balanced Active Learning for Image ClassificationCode1
Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and InstancesCode1
A Comparative Survey of Deep Active LearningCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified