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

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Showing 501–510 of 3073 papers

TitleStatusHype
NE-LP: Normalized Entropy and Loss Prediction based Sampling for Active Learning in Chinese Word Segmentation on EHRs—0
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation—0
A Survey on Curriculum Learning—0
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
Active Regression by Stratification—0
Active Regression via Linear-Sample Sparsification—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings—0
A Bayesian Active Learning Approach to Comparative Judgement—0
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Benchmark Results

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