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 161170 of 3073 papers

TitleStatusHype
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Active learning based generative design for the discovery of wide bandgap materialsCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active Learning Through a Covering LensCode1
Code-free development and deployment of deep segmentation models for digital pathologyCode1
Confidence-Aware Learning for Deep Neural NetworksCode1
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)Code1
Active Imitation Learning with Noisy GuidanceCode1
ALPBench: A Benchmark for Active Learning Pipelines on Tabular DataCode1
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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