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 1791–1800 of 3073 papers

TitleStatusHype
Deep Active Ensemble Sampling For Image Classification—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
Deep Active Learning: A Reality Check—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Deep Active Learning by Leveraging Training Dynamics—0
Deep Active Learning by Model Interpretability—0
Deep Active Learning for Computer Vision: Past and Future—0
Deep Active Learning for Data Mining from Conflict Text Corpora—0
Deep Active Learning for Dialogue Generation—0
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector—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