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 2871–2880 of 3073 papers

Title	Date	Tasks	Status
BatchGFN: Generative Flow Networks for Batch Active Learning	Jun 26, 2023	Active Learning	CodeCode Available
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes	Sep 11, 2023	Active Learning	CodeCode Available
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning	Mar 18, 2022	Active Learning	CodeCode Available
QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image Segmentation	Mar 12, 2025	Active LearningImage Segmentation	CodeCode Available
Exploiting Counter-Examples for Active Learning with Partial labels	Jul 14, 2023	Active Learning	CodeCode Available
Learning Preferences for Interactive Autonomy	Oct 19, 2022	Active LearningAutonomous Driving	CodeCode Available
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models	Jun 13, 2024	Active LearningClassification	CodeCode Available
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank	Feb 17, 2019	Active LearningCrowd Counting	CodeCode Available
Uncertainty quantification for predictions of atomistic neural networks	Jul 14, 2022	Active LearningProperty Prediction	CodeCode Available
Batch Decorrelation for Active Metric Learning	May 20, 2020	Active LearningDiversity	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