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 2311–2320 of 3073 papers

TitleStatusHype
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
Learning switched systems from simulation models—0
Learning the Valuations of a k-demand Agent—0
Learning Time Dependent Choice—0
Learning to Actively Learn: A Robust Approach—0
Learning to Actively Learn Neural Machine Translation—0
Learning to Caption Images Through a Lifetime by Asking Questions—0
Learning to Detect Interesting Anomalies—0
Learning to Explore and Exploit in POMDPs—0
Learning to Learn for Few-shot Continual Active Learning—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