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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1181–1190 of 3073 papers

TitleStatusHype
A Survey of Latent Factor Models in Recommender Systems—0
Active Learning for Continual Learning: Keeping the Past Alive in the Present—0
Active Dictionary Learning in Sparse Representation Based Classification—0
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
A Survey of Active Learning for Natural Language Processing—0
Active Learning for Contextual Search with Binary Feedbacks—0
A survey of active learning algorithms for supervised remote sensing image classification—0
A supervised active learning method for identifying critical nodes in Wireless Sensor Network—0
Active Learning Solution on Distributed Edge Computing—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