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 1351–1360 of 3073 papers

Title	Date	Tasks	Status
Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints	Mar 27, 2025	Active LearningBayesian Optimization	—Unverified
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping	Sep 17, 2014	Active Learning	—Unverified
Amortized Active Learning for Nonparametric Functions	Jul 25, 2024	Active Learning	—Unverified
Discovering and forecasting extreme events via active learning in neural operators	Apr 5, 2022	Active LearningExperimental Design	—Unverified
Active Learning-Based Optimization of Scientific Experimental Design	Dec 29, 2021	Active LearningExperimental Design	—Unverified
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models	Jul 1, 2018	Active Learning	—Unverified
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog	Jul 1, 2020	Active LearningKnowledge Graphs	—Unverified
Amortized nonmyopic active search via deep imitation learning	May 23, 2024	Active LearningImitation Learning	—Unverified
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space	Jan 6, 2023	Active LearningSymbolic Regression	—Unverified
AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization	Apr 21, 2021	Active LearningBayesian Optimization	—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