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 871880 of 3073 papers

TitleStatusHype
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning0
AI For Fraud Awareness0
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno0
Automatic Learning to Detect Concept Drift0
Automatic Playtesting for Game Parameter Tuning via Active Learning0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation0
Active Learning within Constrained Environments through Imitation of an Expert Questioner0
AutoNLU: Detecting, root-causing, and fixing NLU model errors0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping0
Active Learning for Structured Probabilistic Models With Histogram Approximation0
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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