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 701–710 of 3073 papers

Title	Date	Tasks	Status	Hype
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners	Oct 9, 2023	Active LearningClassification	CodeCode Available	0
Improved Active Learning via Dependent Leverage Score Sampling	Oct 8, 2023	Active LearningUncertainty Quantification	—Unverified	0
Guideline Learning for In-context Information Extraction	Oct 8, 2023	Active LearningEvent Extraction	—Unverified	0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy	Oct 5, 2023	Active LearningSemantic Segmentation	—Unverified	0
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design	Oct 5, 2023	Active LearningDrug Design	CodeCode Available	1
Pool-Based Active Learning with Proper Topological Regions	Oct 2, 2023	Active LearningMulti-class Classification	—Unverified	0
Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation	Oct 2, 2023	Active Learning	CodeCode Available	0
Deterministic Langevin Unconstrained Optimization with Normalizing Flows	Oct 1, 2023	Active LearningBayesian Optimization	—Unverified	0
Nonparametric active learning for cost-sensitive classification	Sep 30, 2023	Active LearningClassification	—Unverified	0
Deep Active Learning with Noisy Oracle in Object Detection	Sep 30, 2023	Active LearningObject	—Unverified	0

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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