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 1871–1880 of 3073 papers

TitleStatusHype
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images—0
Discriminative Active Learning for Domain Adaptation—0
Discriminative Batch Mode Active Learning—0
Discwise Active Learning for LiDAR Semantic Segmentation—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Distance-Penalized Active Learning Using Quantile Search—0
Distilling the Posterior in Bayesian Neural Networks—0
Distributed Safe Learning and Planning for Multi-robot Systems—0
Distributional Latent Variable Models with an Application in Active Cognitive Testing—0
Distributionally Robust Active Learning for Gaussian Process Regression—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