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 17511760 of 3073 papers

TitleStatusHype
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection0
Cost-Sensitive Active Learning for Dialogue State Tracking0
CoTAL: Human-in-the-Loop Prompt Engineering, Chain-of-Thought Reasoning, and Active Learning for Generalizable Formative Assessment Scoring0
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases0
CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation0
CRAB Reader: A Tool for Analysis and Visualization of Argumentative Zones in Scientific Literature0
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance0
Critic Loss for Image Classification0
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data0
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop0
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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