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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 1861–1870 of 3073 papers

Title	Date	Tasks	Status
Optimal Sampling Gaps for Adaptive Submodular Maximization	Apr 5, 2021	Active LearningBIG-bench Machine Learning	—Unverified
Efficacy of Bayesian Neural Networks in Active Learning	Apr 2, 2021	Active Learning	CodeCode Available
STARdom: an architecture for trusted and secure human-centered manufacturing systems	Apr 2, 2021	Active LearningDecision Making	—Unverified
Paladin: an annotation tool based on active and proactive learning	Apr 1, 2021	Active Learning	—Unverified
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning	Mar 31, 2021	Active LearningAnomaly Detection	—Unverified
Towards Active Learning Based Smart Assistant for Manufacturing	Mar 30, 2021	Active LearningBIG-bench Machine Learning	—Unverified
Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation	Mar 29, 2021	Active LearningCPU	—Unverified
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries	Mar 27, 2021	Active Learning	—Unverified
Active Structure Learning of Bayesian Networks in an Observational Setting	Mar 25, 2021	Active Learning	CodeCode Available
Data driven semi-supervised learning	Mar 18, 2021	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