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 2101–2110 of 3073 papers

TitleStatusHype
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
Practical applications of metric space magnitude and weighting vectors—0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
MCAL: Minimum Cost Human-Machine Active LabelingCode0
Effective Version Space Reduction for Convolutional Neural Networks—0
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure—0
Fair Active LearningCode0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples—0
Active Learning for Nonlinear System Identification with Guarantees—0
On the Robustness of 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