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 15911600 of 3073 papers

TitleStatusHype
Cartography Active LearningCode1
Importance sampling based active learning for parametric seismic fragility curve estimation0
Active Learning by Acquiring Contrastive ExamplesCode1
AdjointNet: Constraining machine learning models with physics-based codes0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification0
Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning0
Active Learning for Automated Visual Inspection of Manufactured Products0
ALLWAS: Active Learning on Language models in WASserstein space0
Sample Noise Impact on Active LearningCode0
Word Discriminations for Vocabulary Inventory PredictionCode0
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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