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 2501–2510 of 3073 papers

Title	Date	Tasks	Status
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria	Jun 30, 2018	Active Learningobject-detection	CodeCode Available
MetAL: Active Semi-Supervised Learning on Graphs via Meta Learning	Jul 22, 2020	Active LearningBilevel Optimization	CodeCode Available
Zero Initialised Unsupervised Active Learning by Optimally Balanced Entropy-Based Sampling for Imbalanced Problems	May 24, 2021	Active LearningBIG-bench Machine Learning	CodeCode Available
A Flexible Framework for Anomaly Detection via Dimensionality Reduction	Sep 9, 2019	Active LearningAnomaly Detection	CodeCode Available
Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource	Jun 11, 2018	Active LearningCross-Lingual Transfer	CodeCode Available
Active Keyword Selection to Track Evolving Topics on Twitter	Sep 22, 2022	Active Learning	CodeCode Available
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems	Mar 17, 2023	Active LearningGaussian Processes	CodeCode Available
Path-integral molecular dynamics with actively-trained and universal machine learning force fields	May 20, 2025	Active LearningComputational Efficiency	CodeCode Available
A Structural-Clustering Based Active Learning for Graph Neural Networks	Dec 7, 2023	Active LearningClustering	CodeCode Available
Cost-Sensitive Active Learning for Incomplete Data	Jun 23, 2022	Active LearningAttribute	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