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 861–870 of 3073 papers

Title	Date	Tasks	Status
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection	Dec 28, 2023	Active LearningChange Detection	—Unverified
Active Third-Person Imitation Learning	Dec 27, 2023	Active LearningGenerative Adversarial Network	—Unverified
BAL: Balancing Diversity and Novelty for Active Learning	Dec 26, 2023	Active LearningDiversity	CodeCode Available
Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes	Dec 23, 2023	Active Learning	—Unverified
MEAOD: Model Extraction Attack against Object Detectors	Dec 22, 2023	Active Learningmodel	—Unverified
On the Convergence of Loss and Uncertainty-based Active Learning Algorithms	Dec 21, 2023	Active Learning	CodeCode Available
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity	Dec 18, 2023	Active LearningDiversity	—Unverified
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning	Dec 17, 2023	Active LearningOutlier Detection	—Unverified
Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation	Dec 16, 2023	Active LearningDimensionality Reduction	—Unverified
ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)	Dec 15, 2023	Active LearningLanguage Modelling	—Unverified

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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