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 2281–2290 of 3073 papers

TitleStatusHype
Active Learning and the Irish Treebank—0
Active Learning Applied to Patient-Adaptive Heartbeat Classification—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
Active Learning Approach to Optimization of Experimental Control—0
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images—0
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Active Learning-based Model Predictive Coverage Control—0
Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation—0
Show:102550
← PrevPage 229 of 308Next →

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