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 471480 of 3073 papers

TitleStatusHype
Actively Learning Gaussian Process DynamicsCode0
ESA: Annotation-Efficient Active Learning for Semantic SegmentationCode0
Active Learning for Entity AlignmentCode0
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
A Comparison of Strategies for Source-Free Domain AdaptationCode0
Confidence Estimation Using Unlabeled DataCode0
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition CharacteristicsCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical GuaranteesCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
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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