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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 3041–3050 of 3073 papers

Title	Date	Tasks	Status
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables	Apr 10, 2012	Active LearningModel Selection	—Unverified
Distribution-Dependent Sample Complexity of Large Margin Learning	Apr 5, 2012	Active LearningGeneral Classification	—Unverified
Active learning for interactive machine translation	Apr 1, 2012	Active LearningMachine Translation	—Unverified
Bayesian Active Learning for Classification and Preference Learning	Dec 24, 2011	Active LearningClassification	CodeCode Available
Active Learning with a Drifting Distribution	Dec 1, 2011	Active Learning	—Unverified
Active learning of neural response functions with Gaussian processes	Dec 1, 2011	Active LearningExperimental Design	—Unverified
Video Annotation and Tracking with Active Learning	Dec 1, 2011	Active Learning	—Unverified
Bayesian Bias Mitigation for Crowdsourcing	Dec 1, 2011	Active Learning	—Unverified
Online Submodular Set Cover, Ranking, and Repeated Active Learning	Dec 1, 2011	Active Learning	—Unverified
Lower Bounds for Passive and Active Learning	Dec 1, 2011	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