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 1211–1220 of 3073 papers

Title	Date	Tasks	Status	Hype
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities	Aug 1, 2022	Active LearningInformativeness	CodeCode Available	0
Information Gain Sampling for Active Learning in Medical Image Classification	Aug 1, 2022	Active LearningClassification	—Unverified	0
Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging	Jul 31, 2022	Active Learning	—Unverified	0
Deep Active Learning with Budget Annotation	Jul 31, 2022	Active LearningInformativeness	—Unverified	0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge	Jul 29, 2022	Active LearningContrastive Learning	—Unverified	0
Unsupervised Frequent Pattern Mining for CEP	Jul 28, 2022	Active LearningDescriptive	—Unverified	0
ALBench: A Framework for Evaluating Active Learning in Object Detection	Jul 27, 2022	Active Learningimage-classification	CodeCode Available	2
Partial-Monotone Adaptive Submodular Maximization	Jul 26, 2022	Active LearningDecision Making	—Unverified	0
Active Learning of Ordinal Embeddings: A User Study on Football Data	Jul 26, 2022	Active LearningInformation Retrieval	—Unverified	0
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning	Jul 25, 2022	Active LearningDiversity	—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