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 16611670 of 3073 papers

TitleStatusHype
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning0
Optimal Sampling Density for Nonparametric Regression0
Optimal Sampling Gaps for Adaptive Submodular Maximization0
Optimal simulation-based Bayesian decisions0
Optimiser l'adaptation en ligne d'un module de compr\'ehension de la parole avec un algorithme de bandit contre un adversaire (Adversarial bandit for optimising online active learning of spoken language understanding)0
Optimizing Active Learning for Low Annotation Budgets0
Optimizing annotation efforts to build reliable annotated corpora for training statistical models0
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian0
Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks0
Optimizing Features in Active Machine Learning for Complex Qualitative Content Analysis0
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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