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 681–690 of 3073 papers

Title	Date	Tasks	Status	Score
An Active Approach for Model Interpretation	Oct 27, 2019	Active Learningmodel	CodeCode Available	5
Active Learning in CNNs via Expected Improvement Maximization	Nov 27, 2020	Active Learning	CodeCode Available	5
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions	Oct 5, 2022	Active LearningAutonomous Driving	CodeCode Available	5
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis	Sep 5, 2019	Active LearningData Augmentation	CodeCode Available	5
Bayesian Semi-supervised Learning with Graph Gaussian Processes	Sep 12, 2018	Active LearningGaussian Processes	CodeCode Available	5
Empowering Active Learning to Jointly Optimize System and User Demands	May 9, 2020	Active Learning	CodeCode Available	5
A Bayesian Approach for Sequence Tagging with Crowds	Nov 2, 2018	Active LearningArgument Mining	CodeCode Available	5
Active Learning for Non-Parametric Regression Using Purely Random Trees	Dec 1, 2018	Active LearningBinary Classification	CodeCode Available	5
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression	Jul 31, 2023	Active LearningDiversity	CodeCode Available	5
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions	Mar 29, 2017	Active LearningBayesian Inference	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