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 1901–1910 of 3073 papers

Title	Date	Tasks	Status	Hype
Constructing a Korean Named Entity Recognition Dataset for the Financial Domain using Active Learning	Dec 1, 2020	Active Learningnamed-entity-recognition	—Unverified	0
Bilingual Transfer Learning for Online Product Classification	Dec 1, 2020	Active LearningClassification	—Unverified	0
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks	Dec 1, 2020	Active LearningDialogue Generation	—Unverified	0
A Multitask Active Learning Framework for Natural Language Understanding	Dec 1, 2020	Active LearningNatural Language Understanding	—Unverified	0
Counting People by Estimating People Flows	Dec 1, 2020	Active LearningCrowd Counting	CodeCode Available	1
High-contrast “gaudy” images improve the training of deep neural network models of visual cortex	Dec 1, 2020	Active Learning	CodeCode Available	0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes	Nov 30, 2020	Active LearningGaussian Processes	—Unverified	0
On Initial Pools for Deep Active Learning	Nov 30, 2020	Active Learning	CodeCode Available	0
Active Output Selection Strategies for Multiple Learning Regression Models	Nov 29, 2020	Active Learningregression	—Unverified	0
A smartphone based multi input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques	Nov 29, 2020	Active LearningDiagnostic	—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