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 19411950 of 3073 papers

TitleStatusHype
Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning0
Efforts estimation of doctors annotating medical image0
ELAD: Explanation-Guided Large Language Models Active Distillation0
Understanding the Eluder Dimension0
Embodied Active Learning of Relational State Abstractions for Bilevel Planning0
Embodied Learning for Lifelong Visual Perception0
Embodied Visual Active Learning for Semantic Segmentation0
Empirical Evaluation of Active Learning Techniques for Neural MT0
Empirical Evaluations of Active Learning Strategies in Legal Document Review0
Empowering Language Models with Active Inquiry for Deeper Understanding0
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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