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 1141–1150 of 3073 papers

Title	Date	Tasks	Status
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget	Sep 25, 2019	Active LearningRepresentation Learning	—Unverified
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions	Jun 1, 2023	Active Learning	—Unverified
Continuous Active Learning Using Pretrained Transformers	Aug 15, 2022	Active LearningInformation Retrieval	—Unverified
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation	Apr 3, 2023	Active LearningTransfer Learning	—Unverified
Contrastive Coding for Active Learning Under Class Distribution Mismatch	Jan 1, 2021	Active LearningContrastive Learning	—Unverified
Adaptive Submodular Ranking and Routing	Jun 5, 2016	Active Learning	—Unverified
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning	Sep 3, 2020	Active Learning	—Unverified
Convergence Rates of Active Learning for Maximum Likelihood Estimation	Jun 8, 2015	Active LearningBinary Classification	—Unverified
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition	Dec 2, 2024	Active LearningCross-corpus	—Unverified
Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax	Dec 1, 2016	Active LearningNatural Language Inference	—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