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 26312640 of 3073 papers

TitleStatusHype
Active learning for structural reliability: survey, general framework and benchmark0
A general-purpose AI assistant embedded in an open-source radiology information system0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity0
Agnostic Active Learning Without Constraints0
Agnostic Multi-Group Active Learning0
A Graph-Based Approach for Active Learning in Regression0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping0
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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