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 1981–1990 of 3073 papers

Title	Date	Tasks	Status
Support Vector Machines under Adversarial Label Contamination	Jun 1, 2022	Active LearningBIG-bench Machine Learning	—Unverified
Surrogate Losses in Passive and Active Learning	Jul 16, 2012	Active Learning	—Unverified
Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework	Sep 28, 2024	Active LearningDiagnostic	—Unverified
Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization	Feb 9, 2017	Active LearningBrain Computer Interface	—Unverified
Synthesizing Video Trajectory Queries	Oct 8, 2021	Active LearningObject Tracking	—Unverified
Tackling Provably Hard Representative Selection via Graph Neural Networks	May 20, 2022	Active LearningData Compression	—Unverified
TActiLE: Tiny Active LEarning for wearable devices	May 2, 2025	Active Learningimage-classification	—Unverified
Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets	Sep 17, 2014	Active Learning	—Unverified
Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize	Oct 10, 2023	Active LearningModel Predictive Control	—Unverified
Taming Small-sample Bias in Low-budget Active Learning	Jun 19, 2023	Active Learning	—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