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 2651–2660 of 3073 papers

Title	Date	Tasks	Status
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization	May 15, 2015	Active Learning	—Unverified
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper	Feb 2, 2017	Active LearningGPU	—Unverified
ALICE: Active Learning with Contrastive Natural Language Explanations	Sep 22, 2020	Active LearningClassification	—Unverified
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data	Dec 2, 2022	Active LearningGenerative Adversarial Network	—Unverified
Align Me: A framework to generate Parallel Corpus Using OCRs and Bilingual Dictionaries	Dec 1, 2016	Active LearningInformation Retrieval	—Unverified
Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey	May 23, 2025	Active LearningReinforcement Learning (RL)	—Unverified
A Linear Time Active Learning Algorithm for Link Classification	Dec 1, 2012	Active LearningClassification	—Unverified
ALLSH: Active Learning Guided by Local Sensitivity and Hardness	May 10, 2022	Active LearningFew-Shot Learning	—Unverified
ALLWAS: Active Learning on Language models in WASserstein space	Sep 3, 2021	Active Learningtext-classification	—Unverified
ALLWAS: Active Learning on Language models in WASserstein space	Jul 17, 2021	Active Learningtext-classification	—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