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 2391–2400 of 3073 papers

Title	Date	Tasks	Status	Hype
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields	May 16, 2019	Active Learning	—Unverified	0
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models	May 9, 2019	Active LearningBIG-bench Machine Learning	CodeCode Available	0
Learning Loss for Active Learning	May 9, 2019	Active Learningimage-classification	CodeCode Available	1
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors	May 1, 2019	Active Learning	—Unverified	0
Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles	May 1, 2019	Active LearningGeneral Classification	—Unverified	0
Bayesian Generative Active Deep Learning	Apr 26, 2019	Active LearningData Augmentation	—Unverified	0
Factored Contextual Policy Search with Bayesian Optimization	Apr 26, 2019	Active LearningBayesian Optimization	—Unverified	0
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays	Apr 24, 2019	Active LearningData Augmentation	—Unverified	0
Disagreement-based Active Learning in Online Settings	Apr 19, 2019	Active LearningGeneral Classification	—Unverified	0
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning	Apr 18, 2019	Active LearningRepresentation Learning	—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