Confident Learning: Estimating Uncertainty in Dataset Labels

DOI10.1613/jair.1.12125OpenAlexW3156669901MaRDI QIDQ4989326

Lu Jiang, Isaac Chuang, Curtis G. Northcutt

Publication date: 25 May 2021

Published in: Journal of Artificial Intelligence Research (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.00068

zbMATH Keywords

uncertaintymachine learningontologies

Mathematics Subject Classification ID

Artificial intelligence (68Txx)

Related Items (3)

Beyond linear subspace clustering: a comparative study of nonlinear manifold clustering algorithms ⋮ An instance-dependent simulation framework for learning with label noise ⋮ cleanlab

Uses Software

• MNIST
• OpenML
• CIFAR
• MS-COCO
• mixup
• OpenML-Python
• DivideMix
• MentorNet

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• OpenML
• Density Ratio Estimation in Machine Learning
• Distinguishing and integrating aleatoric and epistemic variation in uncertainty quantification
• Paradoxes of fuzzy logic, revisited

This page was built for publication: Confident Learning: Estimating Uncertainty in Dataset Labels