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 11011110 of 3073 papers

TitleStatusHype
Active learning for deep semantic parsing0
ActiveDP: Bridging Active Learning and Data Programming0
AutoNLU: Detecting, root-causing, and fixing NLU model errors0
Active Learning within Constrained Environments through Imitation of an Expert Questioner0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation0
Automatic Playtesting for Game Parameter Tuning via Active Learning0
Active Learning with Importance Sampling0
Active Learning for Deep Object Detection0
Automatic Learning to Detect Concept Drift0
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno0
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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