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cs.LG2026
Disentangled Representation Learning via Flow Matching
Jinjin Chi, Taoping Liu, Mengtao Yin +5
Disentangled representation learning aims to capture the underlying explanatory factors of observed data, enabling a principled understanding of the data-generating process. Recent…
cs.LG2025
Learning Causal Transition Matrix for Instance-dependent Label Noise
Jiahui Li, Tai-Wei Chang, Kun Kuang +3
Noisy labels are both inevitable and problematic in machine learning methods, as they negatively impact models' generalization ability by causing overfitting. In the context of lea…