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 1120 of 3073 papers

TitleStatusHype
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary DataCode2
Physics-informed active learning for accelerating quantum chemical simulationsCode2
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theoryCode2
Active Generalized Category DiscoveryCode2
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented AgentsCode2
Transductive Active Learning: Theory and ApplicationsCode2
Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learningCode2
Multi-Fidelity Active Learning with GFlowNetsCode2
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical ImagesCode2
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three WeeksCode2
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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