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 2511–2520 of 3073 papers

Title	Date	Tasks	Status	Hype
Target-Independent Active Learning via Distribution-Splitting	Sep 28, 2018	Active Learning	—Unverified	0
Generative Adversarial Active Learning for Unsupervised Outlier Detection	Sep 28, 2018	Active LearningBinary Classification	CodeCode Available	0
An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance	Sep 27, 2018	Active LearningAttribute	—Unverified	0
A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria	Sep 27, 2018	Active LearningGeneral Classification	—Unverified	0
Active Learning for Deep Object Detection	Sep 26, 2018	Active LearningIncremental Learning	—Unverified	0
Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines	Sep 25, 2018	Active LearningBinary Classification	—Unverified	0
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis	Sep 25, 2018	Active LearningDiabetic Retinopathy Detection	—Unverified	0
Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization	Sep 24, 2018	Active LearningVideo Summarization	CodeCode Available	0
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems	Sep 21, 2018	Active LearningUncertainty Quantification	—Unverified	0
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	CodeCode Available	1

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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