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 2621–2630 of 3073 papers

Title	Date	Tasks	Status
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa	Mar 12, 2018	Active LearningDeep Learning	—Unverified
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users	Mar 9, 2018	Active LearningBIG-bench Machine Learning	—Unverified
Dimension-Robust MCMC in Bayesian Inverse Problems	Mar 9, 2018	Active LearningEfficient Exploration	—Unverified
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach	Mar 6, 2018	Active LearningInformativeness	—Unverified
Differentiable Submodular Maximization	Mar 5, 2018	Active LearningData Summarization	—Unverified
Deep Bayesian Active Semi-Supervised Learning	Mar 3, 2018	Active LearningData Augmentation	CodeCode Available
Active model learning and diverse action sampling for task and motion planning	Mar 2, 2018	Active LearningMotion Planning	CodeCode Available
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling	Mar 1, 2018	Active Learningreinforcement-learning	—Unverified
Adversarial Active Learning for Deep Networks: a Margin Based Approach	Feb 27, 2018	Active Learning	—Unverified
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning	Feb 27, 2018	Active LearningOptical Character Recognition (OCR)	CodeCode Available

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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