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 2361–2370 of 3073 papers

TitleStatusHype
Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles—0
Active learning for interactive satellite image change detection—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Active learning for level set estimation under cost-dependent input uncertainty—0
Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages—0
Active learning for medical code assignment—0
Active Learning for Multi-class Image Classification—0
Active Learning for Multilingual Fingerspelling Corpora—0
Active Learning for Multilingual Semantic Parser—0
Active Learning for Natural Language Generation—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