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 211–220 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets	Apr 8, 2024	Active Learningimbalanced classification	CodeCode Available	1	5
An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping	Feb 7, 2023	Active LearningSemantic Segmentation	CodeCode Available	1	5
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings	Oct 16, 2020	Active LearningClustering	CodeCode Available	1	5
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains	May 23, 2021	Active LearningBayesian Optimisation	CodeCode Available	1	5
Graph-based Active Learning for Semi-supervised Classification of SAR Data	Mar 31, 2022	Active Learninggraph construction	CodeCode Available	1	5
Graph Policy Network for Transferable Active Learning on Graphs	Jun 24, 2020	Active Learning	CodeCode Available	1	5
Active Learning for Domain Adaptation: An Energy-Based Approach	Dec 2, 2021	Active LearningDomain Adaptation	CodeCode Available	1	5
HUMAN: Hierarchical Universal Modular ANnotator	Oct 2, 2020	Active LearningManagement	CodeCode Available	1	5
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy	Mar 15, 2022	Active LearningClassification	CodeCode Available	1	5
Bayesian active learning for production, a systematic study and a reusable library	Jun 17, 2020	Active Learning	CodeCode Available	1	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