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 19711980 of 3073 papers

TitleStatusHype
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool0
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective0
SeqMix: Augmenting Active Sequence Labeling via Sequence MixupCode1
OLALA: Object-Level Active Learning for Efficient Document Layout AnnotationCode1
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps0
Data-efficient Online Classification with Siamese Networks and Active Learning0
HUMAN: Hierarchical Universal Modular ANnotatorCode1
Neural BootstrapperCode1
Gaussian Process Molecule Property Prediction with FlowMO0
Active Learning for Bayesian 3D Hand Pose EstimationCode1
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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