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 10911100 of 3073 papers

TitleStatusHype
Adaptivity to Noise Parameters in Nonparametric Active Learning0
ActiveAnno: General-Purpose Document-Level Annotation Tool with Active Learning Integration0
Multi-View Active Learning for Short Text Classification in User-Generated Data0
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition0
Convergence of Uncertainty Sampling for Active Learning0
Coresets for Classification – Simplified and Strengthened0
Adaptivity in Adaptive Submodularity0
Adaptive Submodular Ranking and Routing0
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization0
Active learning for imbalanced data under cold start0
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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