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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1471–1480 of 3073 papers

Title	Date	Tasks	Status
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection	Nov 27, 2023	Active LearningBenchmarking	—Unverified
Embodied Active Learning of Relational State Abstractions for Bilevel Planning	Mar 8, 2023	Active LearningInformativeness	—Unverified
Embodied Learning for Lifelong Visual Perception	Dec 28, 2021	Active LearningDeep Reinforcement Learning	—Unverified
Embodied Visual Active Learning for Semantic Segmentation	Dec 17, 2020	Active LearningDeep Reinforcement Learning	—Unverified
Empirical Evaluation of Active Learning Techniques for Neural MT	Nov 1, 2019	Active LearningMachine Translation	—Unverified
Empirical Evaluations of Active Learning Strategies in Legal Document Review	Apr 3, 2019	Active LearningBIG-bench Machine Learning	—Unverified
Annotation Cost Efficient Active Learning for Content Based Image Retrieval	Jun 20, 2023	Active LearningContent-Based Image Retrieval	—Unverified
Empowering Language Models with Active Inquiry for Deeper Understanding	Feb 6, 2024	Active LearningLanguage Modeling	—Unverified
Enhanced Labelling in Active Learning for Coreference Resolution	Dec 1, 2020	Active Learningcoreference-resolution	—Unverified
Compositional Active Learning of Synchronizing Systems through Automated Alphabet Refinement	Apr 23, 2025	Active Learning	—Unverified

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified