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 891900 of 3073 papers

TitleStatusHype
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning0
Active Learning with Oracle Epiphany0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding0
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems0
AI For Fraud Awareness0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping0
Active Learning for Structured Probabilistic Models With Histogram Approximation0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy0
BAOD: Budget-Aware Object Detection0
Active Learning for Structured Prediction from Partially Labeled Data0
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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