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 11–20 of 3073 papers

TitleStatusHype
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
Active Generalized Category DiscoveryCode2
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AICode2
Human-in-the-Loop Large-Scale Predictive Maintenance of WorkstationsCode2
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three WeeksCode2
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary DataCode2
Active Prompting with Chain-of-Thought for Large Language ModelsCode2
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented AgentsCode2
DeepCore: A Comprehensive Library for Coreset Selection in Deep LearningCode2
DCoM: Active Learning for All LearnersCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified