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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 2411–2420 of 3073 papers

Title	Date	Tasks	Status	Hype
Empirical Evaluations of Active Learning Strategies in Legal Document Review	Apr 3, 2019	Active LearningBIG-bench Machine Learning	—Unverified	0
Sequential Adaptive Design for Jump Regression Estimation	Apr 2, 2019	Active Learningregression	—Unverified	0
Active Learning for Network Intrusion Detection	Apr 2, 2019	Active LearningAnomaly Detection	—Unverified	0
An Atomistic Machine Learning Package for Surface Science and Catalysis	Apr 1, 2019	Active LearningBIG-bench Machine Learning	CodeCode Available	0
Variational Adversarial Active Learning	Mar 31, 2019	Active Learningimage-classification	CodeCode Available	1
Active Multi-Information Source Bayesian Quadrature	Mar 27, 2019	Active Learning	—Unverified	0
Active Stacking for Heart Rate Estimation	Mar 26, 2019	Active LearningHeart rate estimation	—Unverified	0
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification	Mar 26, 2019	Active LearningBinary Classification	—Unverified	0
Active Learning of Spin Network Models	Mar 25, 2019	Active Learning	CodeCode Available	0
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints	Mar 21, 2019	Active LearningBayesian Inference	—Unverified	0

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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