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 121130 of 3073 papers

TitleStatusHype
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)Code1
Consistency-based Active Learning for Object DetectionCode1
Counting People by Estimating People FlowsCode1
Active Learning Meets Optimized Item SelectionCode1
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule DiagnosisCode1
CriticLean: Critic-Guided Reinforcement Learning for Mathematical FormalizationCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
Accelerating high-throughput virtual screening through molecular pool-based active learningCode1
Active Learning from the WebCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
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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