3 papers
cs.LG2023
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
Yuwen Li, Miao Xiong, Bryan Hooi
Label errors have been found to be prevalent in popular text, vision, and audio datasets, which heavily influence the safe development and evaluation of machine learning algorithms…
cs.LG2023
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement
Ailin Deng, Miao Xiong, Bryan Hooi
Reliable application of machine learning is of primary importance to the practical deployment of deep learning methods. A fundamental challenge is that models are often unreliable…
cs.LG2023
Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness
Ailin Deng, Shen Li, Miao Xiong +2
Trustworthy machine learning is of primary importance to the practical deployment of deep learning models. While state-of-the-art models achieve astonishingly good performance in t…