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 1961–1970 of 3073 papers

Title	Date	Tasks	Status
Deep Multi-Fidelity Active Learning of High-dimensional Outputs	Dec 2, 2020	Active LearningVocal Bursts Intensity Prediction	—Unverified
High-contrast “gaudy” images improve the training of deep neural network models of visual cortex	Dec 1, 2020	Active Learning	CodeCode Available
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles	Dec 1, 2020	Active LearningArticles	—Unverified
Constructing a Korean Named Entity Recognition Dataset for the Financial Domain using Active Learning	Dec 1, 2020	Active Learningnamed-entity-recognition	—Unverified
Bilingual Transfer Learning for Online Product Classification	Dec 1, 2020	Active LearningClassification	—Unverified
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks	Dec 1, 2020	Active LearningDialogue Generation	—Unverified
A Multitask Active Learning Framework for Natural Language Understanding	Dec 1, 2020	Active LearningNatural Language Understanding	—Unverified
Enhanced Labelling in Active Learning for Coreference Resolution	Dec 1, 2020	Active Learningcoreference-resolution	—Unverified
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes	Nov 30, 2020	Active LearningGaussian Processes	—Unverified
On Initial Pools for Deep Active Learning	Nov 30, 2020	Active Learning	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