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 2321–2330 of 3073 papers

TitleStatusHype
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Learning to Make Decisions via Submodular Regularization—0
Learning to Rank for Active Learning: A Listwise Approach—0
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization—0
Learning to Sample: an Active Learning Framework—0
Learning User's confidence for active learning—0
Learning Weighted Finite Automata over the Max-Plus Semiring and its Termination—0
Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging—0
Learning with a Drifting Target Concept—0
Learning with Labeling Induced Abstentions—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