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 2130 of 3073 papers

TitleStatusHype
Streaming Active Learning with Deep Neural NetworksCode2
Active Prompting with Chain-of-Thought for Large Language ModelsCode2
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowCode2
POTATO: The Portable Text Annotation ToolCode2
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
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
DeepCore: A Comprehensive Library for Coreset Selection in Deep LearningCode2
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical ImagesCode2
CriticLean: Critic-Guided Reinforcement Learning for Mathematical FormalizationCode1
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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