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 651–660 of 3073 papers

Title	Date	Tasks	Status	Score
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation	Jul 14, 2023	Active LearningInformativeness	CodeCode Available	5
A Bayesian Approach for Sequence Tagging with Crowds	Nov 2, 2018	Active LearningArgument Mining	CodeCode Available	5
DeMuX: Data-efficient Multilingual Learning	Nov 10, 2023	Active Learning	CodeCode Available	5
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition	Jun 8, 2025	Active LearningBayesian Inference	CodeCode Available	5
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions	Oct 5, 2022	Active LearningAutonomous Driving	CodeCode Available	5
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture	May 22, 2023	Active LearningDecision Making	CodeCode Available	5
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers	Aug 23, 2019	Active LearningCross-Lingual Transfer	CodeCode Available	5
Active Learning from Positive and Unlabeled Data	Feb 24, 2016	Active LearningInformativeness	CodeCode Available	5
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog System	Sep 1, 2024	Active LearningInformativeness	CodeCode Available	5
Bayesian Batch Active Learning as Sparse Subset Approximation	Aug 6, 2019	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