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 2871–2880 of 3073 papers

TitleStatusHype
Multi-armed Bandit Problem with Known Trend—0
Introducing Geometry in Active Learning for Image Segmentation—0
Recognizing Extended Spatiotemporal Expressions by Actively Trained Average Perceptron Ensembles—0
From Cutting Planes Algorithms to Compression Schemes and Active Learning—0
Multi-Label Active Learning from Crowds—0
Active Learning for Entity Filtering in Microblog StreamsCode0
Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing—0
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection—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