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 1301–1310 of 3073 papers

TitleStatusHype
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation—0
Annotation-Efficient Polyp Segmentation via Active Learning—0
Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process—0
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction—0
Active learning of digenic functions with boolean matrix logic programming—0
Annotating named entities in clinical text by combining pre-annotation and active learning—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