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 2531–2540 of 3073 papers

TitleStatusHype
Active Metric Learning for Supervised Classification—0
Active Metric Learning from Relative Comparisons—0
Active Mining Sample Pair Semantics for Image-text Matching—0
Active Model Aggregation via Stochastic Mirror Descent—0
Active Multi-Information Source Bayesian Quadrature—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
Active Multi-Task Representation Learning—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection—0
Active operator learning with predictive uncertainty quantification for partial differential equations—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