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 1021–1030 of 3073 papers

TitleStatusHype
Can Active Learning Experience Be Transferred?—0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning—0
Active Learning for Sound Event Detection—0
A general-purpose AI assistant embedded in an open-source radiology information system—0
Active learning for structural reliability: survey, general framework and benchmark—0
Active Learning for Skewed Data Sets—0
Active Learning Applied to Patient-Adaptive Heartbeat Classification—0
Deep Active Learning for Anomaly Detection—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities—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