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 2021–2030 of 3073 papers

Title	Date	Tasks	Status
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning	Nov 2, 2022	Active LearningBayesian Optimization	—Unverified
Fase-AL -- Adaptation of Fast Adaptive Stacking of Ensembles for Supporting Active Learning	Jan 30, 2020	Active LearningClassification	—Unverified
Fast active learning for pure exploration in reinforcement learning	Jul 27, 2020	Active Learningreinforcement-learning	—Unverified
Fast and easy language understanding for dialog systems with Microsoft Language Understanding Intelligent Service (LUIS)	Sep 1, 2015	Active Learning	—Unverified
Fast Design Space Exploration of Nonlinear Systems: Part I	Apr 5, 2021	Active LearningBayesian Optimization	—Unverified
Fast Design Space Exploration of Nonlinear Systems: Part II	Apr 5, 2021	Active Learning	—Unverified
FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training	Apr 3, 2025	Active Learning	—Unverified
Fast Interactive Image Retrieval using large-scale unlabeled data	Feb 12, 2018	Active LearningBinary Classification	—Unverified
Fast kNN mode seeking clustering applied to active learning	Dec 20, 2017	Active LearningClustering	—Unverified
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning	Oct 13, 2021	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