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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 2941–2950 of 3073 papers

Title	Date	Tasks	Status
Targeting Optimal Active Learning via Example Quality	Jul 30, 2014	Active LearningGeneral Classification	—Unverified
Anytime Active Learning	Jul 27, 2014	Active LearningDocument Classification	CodeCode Available
Bayesian Nonparametric Crowdsourcing	Jul 18, 2014	Active Learning	—Unverified
Beyond Disagreement-based Agnostic Active Learning	Jul 10, 2014	Active LearningGeneral Classification	—Unverified
Learning Privately with Labeled and Unlabeled Examples	Jul 10, 2014	Active Learning	—Unverified
Active Learning and Best-Response Dynamics	Jun 25, 2014	Active LearningDenoising	—Unverified
Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition	Jun 20, 2014	Active Learning	—Unverified
Active Learning with Constrained Topic Model	Jun 1, 2014	Active Learningmodel	—Unverified
Structuring Operative Notes using Active Learning	Jun 1, 2014	Active LearningSemantic Textual Similarity	—Unverified
Design of an Active Learning System with Human Correction for Content Analysis	Jun 1, 2014	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