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 781–790 of 3073 papers

Title	Date	Tasks	Status	Score
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages	Apr 2, 2024	Active LearningLanguage Modeling	CodeCode Available	5
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability	Aug 16, 2021	Active LearningGaussian Processes	CodeCode Available	5
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation	Jun 21, 2021	Active LearningEvent Argument Extraction	CodeCode Available	5
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images	Sep 11, 2023	Active LearningArtery/Veins Retinal Vessel Segmentation	CodeCode Available	5
Active Learning for Neural Machine Translation	Dec 30, 2022	Active LearningMachine Translation	CodeCode Available	5
Bayesian Semi-supervised Learning with Graph Gaussian Processes	Sep 12, 2018	Active LearningGaussian Processes	CodeCode Available	5
A Bayesian Approach for Sequence Tagging with Crowds	Nov 2, 2018	Active LearningArgument Mining	CodeCode Available	5
Bayesian Dark Knowledge	Jun 14, 2015	Active Learningparameter estimation	CodeCode Available	5
Bayesian Batch Active Learning as Sparse Subset Approximation	Aug 6, 2019	Active Learning	CodeCode Available	5
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning	Apr 19, 2024	Active Learning	CodeCode Available	5

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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