Influence Functions 相关 AI 资讯

11:12

官方账号arXiv cs.LG@Nikki Grens, Luís F. Simões, Kai Hou Yip, Theresa Lueftinger

This paper introduces a new method for training data attribution in machine learning models used in space missions like ESA's Ariel. It reformulates influence in terms of prediction, computes infinitesimal prediction influence efficiently, and derives a conservative error proxy. The method is evaluated against simulated spectra and shows strong correlation with spectral errors. It also identifies influential samples and approximates harmful ones, suggesting its potential as an operational framework for scientific machine learning.

推荐理由：This paper presents a novel approach to data attribution and error proxying for machine learning models in space missions, offering a more efficient and accurate way to assess model performance. It's a must-read for those interested in explainable AI and its applications in scientific research.

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