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 2031–2040 of 3073 papers

Title	Date	Tasks	Status
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification	Jan 26, 2019	Active LearningGeneral Classification	—Unverified
The Utility of Abstaining in Binary Classification	Dec 26, 2015	Active LearningBinary Classification	—Unverified
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration	Jan 30, 2024	3D Object DetectionActive Learning	—Unverified
The Work Capacity of Channels with Memory: Maximum Extractable Work in Percept-Action Loops	Apr 8, 2025	Active Learning	—Unverified
THMA: Tencent HD Map AI System for Creating HD Map Annotations	Dec 14, 2022	Active LearningWeakly-supervised Learning	—Unverified
Thompson Sampling for Dynamic Pricing	Feb 8, 2018	Active LearningThompson Sampling	—Unverified
Thompson sampling for improved exploration in GFlowNets	Jun 30, 2023	Active LearningDecision Making	—Unverified
Ticket-BERT: Labeling Incident Management Tickets with Language Models	Jun 30, 2023	Active LearningLanguage Modeling	—Unverified
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning	Feb 21, 2022	Active Learning	—Unverified
TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis	Dec 18, 2019	Active LearningNeural Network Compression	—Unverified

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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