Gonen Singer

Misclassifying medical conditions can have severe consequences, and the Ordinal Cross-Entropy framework significantly reduces prediction errors by respecting the ordinal nature of disease severity.

CAHP reveals that pruning attention heads through a graph-theoretical approach can enhance model efficiency without sacrificing performance, outperforming traditional methods in high-compression settings.