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 2761–2770 of 3073 papers

Title	Date	Tasks	Status
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks	Feb 2, 2023	Active LearningDecision Making	—Unverified
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images	Jul 3, 2019	Active LearningCollaborative Filtering	—Unverified
A Simple Approximation Algorithm for Optimal Decision Tree	May 21, 2025	Active LearningMedical Diagnosis	—Unverified
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment	Oct 17, 2024	Active LearningContrastive Learning	—Unverified
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification	Sep 30, 2020	Active LearningData Augmentation	—Unverified
A smartphone based multi input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques	Nov 29, 2020	Active LearningDiagnostic	—Unverified
A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature	Nov 1, 2020	Active Learning	—Unverified
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation	Jun 18, 2019	Active LearningImage Segmentation	—Unverified
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction	Apr 7, 2023	Active LearningPrediction	—Unverified
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization	Nov 6, 2019	Active LearningBIG-bench Machine 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