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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 51–60 of 3073 papers

Title	Date	Tasks	Status	Hype
A Comparative Survey of Deep Active Learning	Mar 25, 2022	Active LearningSurvey	CodeCode Available	1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images	Oct 15, 2021	Active LearningLand Cover Classification	CodeCode Available	1
Enhanced spatio-temporal electric load forecasts using less data with active deep learning	Dec 8, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	1
ActiveNeRF: Learning where to See with Uncertainty Estimation	Sep 18, 2022	Active LearningNeRF	CodeCode Available	1
Active Learning for Optimal Intervention Design in Causal Models	Sep 10, 2022	Active LearningExperimental Design	CodeCode Available	1
Active Learning for Coreference Resolution using Discrete Annotation	Apr 28, 2020	Active LearningClustering	CodeCode Available	1
Active Statistical Inference	Mar 5, 2024	Active Learningvalid	CodeCode Available	1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation	Feb 14, 2022	Active Learning	CodeCode Available	1
A Benchmark on Uncertainty Quantification for Deep Learning Prognostics	Feb 9, 2023	Active LearningDecision Making	CodeCode Available	1
Active Learning for Convolutional Neural Networks: A Core-Set Approach	Aug 1, 2017	Active Learningimage-classification	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