Jaeyong Bae

Dataset Complexity Shapes Finite-Distance Loss Geometry in Neural Networks

This work connects this dataset complexity to loss-landscape geometry by pairing local label mixing across neighborhood scales with local entropy around trained neural-network solutions to show that dataset structure shapes how low-loss neighborhoods are organized across finite distances from trained solutions.