2 papers
cs.LG2026
Cross-Fusion Distance: A Novel Metric for Measuring Fusion and Separability Between Data Groups in Representation Space
Xiaolong Zhang, Jianwei Zhang, Xubo Song
Quantifying degrees of fusion and separability between data groups in representation space is a fundamental problem in representation learning, particularly under domain shift. A m…
cs.LG2024
Data Valuation with Gradient Similarity
Nathaniel J. Evans, Gordon B. Mills, Guanming Wu +2
High-quality data is crucial for accurate machine learning and actionable analytics, however, mislabeled or noisy data is a common problem in many domains. Distinguishing low- from…