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

Showing 591600 of 3073 papers

TitleStatusHype
Automated Discovery of Pairwise Interactions from Unstructured Data0
STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning0
A Scalable Algorithm for Active Learning0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning0
Bounds on the Generalization Error in Active Learning0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study0
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object DetectionCode0
Interactive Machine Teaching by Labeling Rules and Instances0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity0
Active learning for regression in engineering populations: A risk-informed approach0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified