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 61–70 of 3073 papers

Title	Date	Tasks	Status	Hype
Inconsistency-Based Data-Centric Active Open-Set Annotation	Jan 10, 2024	Active Learningvalid	CodeCode Available	1
Weakly Supervised Point Cloud Semantic Segmentation via Artificial Oracle	Jan 1, 2024	Active LearningSemantic Segmentation	CodeCode Available	1
Entropic Open-set Active Learning	Dec 21, 2023	Active Learning	CodeCode Available	1
Generalized Category Discovery with Large Language Models in the Loop	Dec 18, 2023	Active LearningContrastive Learning	CodeCode Available	1
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning	Dec 7, 2023	Active LearningSegmentation	CodeCode Available	1
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts	Dec 5, 2023	Active LearningDiversity	CodeCode Available	1
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials	Dec 3, 2023	Active Learning	CodeCode Available	1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations	Dec 2, 2023	Active LearningGaussian Processes	CodeCode Available	1
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models	Nov 27, 2023	Active LearningIn-Context Learning	CodeCode Available	1
Evidential Uncertainty Quantification: A Variance-Based Perspective	Nov 19, 2023	Active LearningClassification	CodeCode Available	1

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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