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 2861–2870 of 3073 papers

TitleStatusHype
Fine-Grained Product Class Recognition for Assisted Shopping—0
Active Learning from Weak and Strong Labelers—0
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks—0
Learning in Unlabeled Networks - An Active Learning and Inference Approach—0
Distance-Penalized Active Learning Using Quantile Search—0
Incremental Active Opinion Learning Over a Stream of Opinionated Documents—0
Improving Event Detection with Active Learning—0
Efficient Named Entity Annotation through Pre-empting—0
Experiments on Active Learning for Croatian Word Sense Disambiguation—0
Fast and easy language understanding for dialog systems with Microsoft Language Understanding Intelligent Service (LUIS)—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