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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 811–820 of 3073 papers

Title	Date	Tasks	Status	Score
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching	Sep 2, 2024	Active Learningscientific discovery	CodeCode Available	5
Active Structure Learning of Bayesian Networks in an Observational Setting	Mar 25, 2021	Active Learning	CodeCode Available	5
A Structural-Clustering Based Active Learning for Graph Neural Networks	Dec 7, 2023	Active LearningClustering	CodeCode Available	5
Nearest Neighbor Classifier with Margin Penalty for Active Learning	Mar 17, 2022	Active Learning	CodeCode Available	5
Bayesian Active Learning By Distribution Disagreement	Jan 2, 2025	Active Learningregression	CodeCode Available	5
Bayesian active learning for optimization and uncertainty quantification in protein docking	Jan 31, 2019	Active LearningBinary Classification	CodeCode Available	5
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning	Jun 19, 2019	Active Learning	CodeCode Available	5
Batch Active Preference-Based Learning of Reward Functions	Oct 10, 2018	Active Learning	CodeCode Available	5
Batch Decorrelation for Active Metric Learning	May 20, 2020	Active LearningDiversity	CodeCode Available	5
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference	Dec 7, 2024	Active Learning	CodeCode Available	5

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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