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 71–80 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Prompt Learning in Vision Language Models	Nov 18, 2023	Active LearningPrompt Learning	CodeCode Available	1
Active Transfer Learning for Efficient Video-Specific Human Pose Estimation	Nov 8, 2023	Active LearningPose Estimation	CodeCode Available	1
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection	Oct 30, 2023	Active LearningDiversity	CodeCode Available	1
LLMaAA: Making Large Language Models as Active Annotators	Oct 30, 2023	Active LearningDataset Generation	CodeCode Available	1
A comprehensive survey on deep active learning in medical image analysis	Oct 22, 2023	Active LearningInformativeness	CodeCode Available	1
Open-CRB: Towards Open World Active Learning for 3D Object Detection	Oct 16, 2023	3D Object DetectionActive Learning	CodeCode Available	1
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design	Oct 5, 2023	Active LearningDrug Design	CodeCode Available	1
Towards Free Data Selection with General-Purpose Models	Sep 29, 2023	Active Learning	CodeCode Available	1
Explaining Predictive Uncertainty with Information Theoretic Shapley Values	Sep 21, 2023	Active LearningDecision Making Under Uncertainty	CodeCode Available	1
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation	Sep 11, 2023	Active LearningDrug Discovery	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