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 2181–2190 of 3073 papers

Title	Date	Tasks	Status
Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise	Feb 10, 2021	Active Learning	—Unverified
Improving Active Learning in Systematic Reviews	Jan 29, 2018	Active Learning	—Unverified
Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty	Dec 11, 2024	Active LearningBinary Classification	—Unverified
Improving Classification-Based Natural Language Understanding with Non-Expert Annotation	Jun 1, 2014	Active LearningClassification	—Unverified
Improving Classification Performance With Human Feedback: Label a few, we label the rest	Jan 17, 2024	Active Learningtext-classification	—Unverified
Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education	Oct 23, 2020	Active LearningGeneral Classification	—Unverified
Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages	Jan 16, 2022	Active LearningAnswer Generation	—Unverified
Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers	Jun 23, 2022	Active LearningDecision Making	—Unverified
Improving Differentially Private Models with Active Learning	Oct 2, 2019	Active Learning	—Unverified
Improving Event Detection with Active Learning	Sep 1, 2015	Active LearningEvent Detection	—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