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 201210 of 3073 papers

TitleStatusHype
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
Active Learning Meets Optimized Item SelectionCode1
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple HumansCode1
Efficient Process Reward Model Training via Active LearningCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active learning for medical image segmentation with stochastic batchesCode1
Explaining Predictive Uncertainty with Information Theoretic Shapley ValuesCode1
FAMIE: A Fast Active Learning Framework for Multilingual Information ExtractionCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
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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