CORTEXA
← Browse

Róisín Luo

2 papers indexed

arxivstat.MLcs.LG2026-07-15

Lipschitz Continuity in Deep Learning: A Systematic Review of Theoretical Foundations, Estimation Methods, Regularization Approaches, and Certifiable Robustness

Róisín Luo, James McDermott, Colm O'Riordan

Lipschitz continuity is a fundamental property of neural networks that characterizes their sensitivity to input perturbations. It plays a pivotal role in deep learning, governing \textbf{robustness}, \textbf{generalization} and \textbf{optimization dynamics}. Despite its importan…

View free PDFSource page
arxivstat.MLcs.AIcs.LG2026-06-29

A Stochastic--Geometric Theory of Scaling Laws in Grokking

Róisín Luo, Christian Gagné, Jonas Ngnawé, Ihsan Ullah, Karyn Morrissey

Delayed generalization (\ie~grokking) refers to the phenomenon in which a neural network fits its training data early in training but only begins to generalize after a prolonged delay, often through an abrupt transition. Despite extensive empirical study, its underlying mechanism…

View free PDFSource page