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 2351–2360 of 3073 papers

TitleStatusHype
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Active Learning for Graphs with Noisy Structures—0
Active Learning for High-Dimensional Binary Features—0
Active Learning for Human Pose Estimation—0
Active Learning for Identification of Linear Dynamical Systems—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Active Learning for Imbalanced Civil Infrastructure Data—0
Active Learning for Imbalanced Sentiment Classification—0
Active learning for interactive machine translation—0
Active Learning for Interactive Neural Machine Translation of Data Streams—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