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 2831–2840 of 3073 papers

Title	Date	Tasks	Status
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective	May 8, 2025	Active LearningBenchmarking	CodeCode Available
On Dataset Transferability in Active Learning for Transformers	May 16, 2023	Active Learningtext-classification	CodeCode Available
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment Recognition	Mar 29, 2020	Active LearningActivity Recognition	CodeCode Available
Entity Alignment with Noisy Annotations from Large Language Models	May 27, 2024	Active LearningEntity Alignment	CodeCode Available
Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning	Jul 7, 2023	Active LearningOutlier Detection	CodeCode Available
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition	Nov 20, 2015	Active LearningFine-Grained Image Classification	CodeCode Available
Bayesian active learning for optimization and uncertainty quantification in protein docking	Jan 31, 2019	Active LearningBinary Classification	CodeCode Available
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence	Jul 26, 2024	Active LearningComputational Efficiency	CodeCode Available
Active Learning of Spin Network Models	Mar 25, 2019	Active Learning	CodeCode Available
Learning Active Learning from Data	Mar 9, 2017	Active Learningregression	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