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 2731–2740 of 3073 papers

Title	Date	Tasks	Status
A Novel Ensemble Learning Approach to Unsupervised Record Linkage	Nov 1, 2017	Active LearningDiversity	—Unverified
A Novel Two-Step Fine-Tuning Pipeline for Cold-Start Active Learning in Text Classification Tasks	Jul 24, 2024	Active LearningDomain Adaptation	—Unverified
Fair Active Learning: Solving the Labeling Problem in Insurance	Dec 17, 2021	Active LearningFairness	—Unverified
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning	Nov 10, 2019	Active LearningBIG-bench Machine Learning	—Unverified
基於多模態主動式學習法進行需備標記樣本之挑選用於候用校長評鑑之自動化評分系統建置(A Multimodal Active Learning Approach toward Identifying Samples to Label during the Development of Automatic Oral Presentation Assessment System for Pre-service Principals Certification Program)[In Chinese]	Oct 1, 2016	Active Learning	—Unverified
A physics-based data-driven model for CO_2 gas diffusion electrodes to drive automated laboratories	Feb 10, 2025	Active Learning	—Unverified
A Pipeline for Post-Crisis Twitter Data Acquisition	Jan 17, 2018	Active LearningBenchmarking	—Unverified
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields	Oct 30, 2023	Active Learning	—Unverified
APLenty: annotation tool for creating high-quality datasets using active and proactive learning	Nov 1, 2018	Active LearningMulti-Label Learning	—Unverified
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design	Dec 8, 2020	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