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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 2831–2840 of 3073 papers

Title	Date	Tasks	Status
Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data	Apr 12, 2016	Active Learning	—Unverified
Active Learning for Online Recognition of Human Activities from Streaming Videos	Apr 11, 2016	Active Learning	—Unverified
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification	Mar 30, 2016	Active Learning	—Unverified
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint	Mar 30, 2016	Active Learning	—Unverified
Active Algorithms For Preference Learning Problems with Multiple Populations	Mar 14, 2016	Active Learning	—Unverified
Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting	Mar 11, 2016	Active Learning	—Unverified
Active Learning from Positive and Unlabeled Data	Feb 24, 2016	Active LearningInformativeness	CodeCode Available
Submodular Learning and Covering with Response-Dependent Costs	Feb 23, 2016	Active Learning	—Unverified
Search Improves Label for Active Learning	Feb 23, 2016	Active Learning	—Unverified
Online Active Linear Regression via Thresholding	Feb 9, 2016	Active Learningregression	—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