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 26612670 of 3073 papers

TitleStatusHype
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait SketchingCode0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change DebateCode0
STONE: A Submodular Optimization Framework for Active 3D Object DetectionCode0
Sample Noise Impact on Active LearningCode0
Active Structure Learning of Bayesian Networks in an Observational SettingCode0
Detecting value-expressive text posts in Russian social mediaCode0
Multi-Resolution Active Learning of Fourier Neural OperatorsCode0
Building a comprehensive syntactic and semantic corpus of Chinese clinical textsCode0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
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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