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 961970 of 3073 papers

TitleStatusHype
Dirichlet-based Uncertainty Calibration for Active Domain AdaptationCode1
A Survey on Uncertainty Quantification Methods for Deep Learning0
Deep active learning for nonlinear system identification0
Active Prompting with Chain-of-Thought for Large Language ModelsCode2
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models0
Deep Active Learning in the Presence of Label Noise: A Survey0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset0
Correlation Clustering with Active Learning of Pairwise Similarities0
Black-Box Batch Active Learning for RegressionCode0
Active learning for data streams: a survey0
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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