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 2201–2210 of 3073 papers

TitleStatusHype
Improving the quality control of seismic data through active learning—0
Improving Uncertainty Sampling with Bell Curve Weight Function—0
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems—0
'In-Between' Uncertainty in Bayesian Neural Networks—0
Incentive Compatible Active Learning—0
Incentivized Collaboration in Active Learning—0
Inconsistency-based Active Learning for LiDAR Object Detection—0
Incorporating Unlabeled Data into Distributionally Robust Learning—0
Incorporating Unlabelled Data into Bayesian Neural Networks—0
Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency—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