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 2061–2070 of 3073 papers

Title	Date	Tasks	Status
From Cutting Planes Algorithms to Compression Schemes and Active Learning	Aug 12, 2015	Active Learning	—Unverified
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)	Apr 26, 2022	Active Learning	—Unverified
From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant	Sep 24, 2024	Active Learning	—Unverified
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System	Apr 21, 2025	Active LearningData Augmentation	—Unverified
From Selection to Generation: A Survey of LLM-based Active Learning	Feb 17, 2025	Active LearningSurvey	—Unverified
From Weakly Supervised Learning to Active Learning	Sep 23, 2022	Active LearningWeakly-supervised Learning	—Unverified
Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection	Mar 22, 2022	Active LearningChange Detection	—Unverified
Frugal Reinforcement-based Active Learning	Dec 9, 2022	Active LearningDiversity	—Unverified
Frugal Satellite Image Change Detection with Deep-Net Inversion	Sep 26, 2023	Active LearningChange Detection	—Unverified
Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation	Jul 18, 2021	Active LearningBIG-bench Machine 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