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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 1361–1370 of 3073 papers

Title	Date	Tasks	Status	Hype
Can I see an Example? Active Learning the Long Tail of Attributes and Relations	Mar 11, 2022	Active Learning	—Unverified	0
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets	Mar 10, 2022	Active Learning	—Unverified	0
Optical Flow Training under Limited Label Budget via Active Learning	Mar 9, 2022	Active LearningOptical Flow Estimation	CodeCode Available	1
Reinforced Meta Active Learning	Mar 9, 2022	Active LearningInformativeness	—Unverified	0
Onception: Active Learning with Expert Advice for Real World Machine Translation	Mar 9, 2022	Active LearningMachine Translation	CodeCode Available	0
Active Self-Semi-Supervised Learning for Few Labeled Samples	Mar 9, 2022	Active Learningimage-classification	—Unverified	0
Boosting the Learning for Ranking Patterns	Mar 5, 2022	Active LearningDecision Making	—Unverified	0
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation	Mar 4, 2022	Active Learning	CodeCode Available	1
Passive and Active Learning of Driver Behavior from Electric Vehicles	Mar 4, 2022	Active LearningInformativeness	—Unverified	0
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions	Mar 3, 2022	Active LearningMultiple-choice	—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