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 2971–2980 of 3073 papers

Title	Date	Tasks	Status
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages	Apr 2, 2024	Active LearningLanguage Modeling	CodeCode Available
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]	Jun 1, 2018	Active LearningAttribute	CodeCode Available
Reducing the Long Tail Losses in Scientific Emulations with Active Learning	Nov 15, 2021	Active LearningDeep Learning	CodeCode Available
TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation	Jan 16, 2023	Active LearningImage Segmentation	CodeCode Available
Active Learning for Abstractive Text Summarization	Jan 9, 2023	Abstractive Text SummarizationActive Learning	CodeCode Available
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability	Aug 16, 2021	Active LearningGaussian Processes	CodeCode Available
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning	Feb 4, 2024	Active LearningClustering	CodeCode Available
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients	Oct 12, 2020	Active LearningAstronomy	CodeCode Available
Using active learning to expand training data for implicit discourse relation recognition	Oct 1, 2018	Active LearningRelation	CodeCode Available
Automated wildlife image classification: An active learning tool for ecological applications	Mar 28, 2023	Active Learningimage-classification	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