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 2911–2920 of 3073 papers

Title	Date	Tasks	Status
Falcon: Fair Active Learning using Multi-armed Bandits	Jan 23, 2024	Active LearningAttribute	CodeCode Available
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering	Sep 21, 2022	Active LearningClustering	CodeCode Available
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning	Jun 19, 2019	Active Learning	CodeCode Available
Re-Benchmarking Pool-Based Active Learning for Binary Classification	Jun 15, 2023	Active LearningBenchmarking	CodeCode Available
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression	Jul 31, 2023	Active LearningDiversity	CodeCode Available
Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid	Jun 4, 2025	Active Learning	CodeCode Available
TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object Detection	Dec 25, 2024	3D Object DetectionActive Learning	CodeCode Available
Batch Active Preference-Based Learning of Reward Functions	Oct 10, 2018	Active Learning	CodeCode Available
Batch Active Learning Using Determinantal Point Processes	Jun 19, 2019	Active LearningDiversity	CodeCode Available
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments	Sep 20, 2021	Active Learning	CodeCode Available

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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