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 621–630 of 3073 papers

Title	Date	Tasks	Status	Hype
Transferable Candidate Proposal with Bounded Uncertainty	Dec 7, 2023	Active LearningExperimental Design	CodeCode Available	0
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning	Dec 7, 2023	Active LearningSegmentation	CodeCode Available	1
A Structural-Clustering Based Active Learning for Graph Neural Networks	Dec 7, 2023	Active LearningClustering	CodeCode Available	0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes	Dec 5, 2023	Active LearningChange Detection	—Unverified	0
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts	Dec 5, 2023	Active LearningDiversity	CodeCode Available	1
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning	Dec 4, 2023	Active LearningVulnerability Detection	—Unverified	0
Federated Active Learning for Target Domain Generalisation	Dec 4, 2023	Active LearningFederated Learning	CodeCode Available	0
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation	Dec 4, 2023	Active LearningSemantic Segmentation	—Unverified	0
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials	Dec 3, 2023	Active Learning	CodeCode Available	1
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors	Dec 3, 2023	Active LearningInstance Segmentation	—Unverified	0

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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