11 papers
Assessing flood-related wellbeing from public discourse in Ireland using large language model as judge, 2012--2025
Róisín Luo, Karyn Morrissey
Flooding is an environmental hazard that produces social and psychological consequences for exposed individuals, shaping threat appraisal, perceived coping capacity, access to soci…
A population-level assessment framework for flood-related wellbeing from public discourse in Ireland
Róisín Luo, Karyn Morrissey
\ textbf{Background.} Ireland's position on the eastern North Atlantic exposes it to moisture-laden westerlies and frequent low-pressure systems, generating abundant precipitation…
ZetaGPT: A Reference Implementation of Positional--Encoding--Free State--Space--Attention Language Models
Róisín Luo
Transformer-based language models rely on self-attention, whose computation is permutation-equivariant and therefore lacks an intrinsic mechanism for representing token order. Exis…
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 \…
A Stochastic--Geometric Theory of Scaling Laws in Grokking
Róisín Luo, Christian Gagné, Jonas Ngnawé +2
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 de…
Principles of Lipschitz continuity in neural networks
Róisín Luo
Deep learning has achieved remarkable success across a wide range of domains, significantly expanding the frontiers of what is achievable in artificial intelligence. Yet, despite t…
Sampling Matters in Explanations: Towards Trustworthy Attribution Analysis Building Block in Visual Models through Maximizing Explanation Certainty
Róisín Luo, James McDermott, Colm O'Riordan
Image attribution analysis seeks to highlight the feature representations learned by visual models such that the highlighted feature maps can reflect the pixel-wise importance of i…
Optimization-Induced Dynamics of Lipschitz Continuity in Neural Networks
Róisín Luo, James McDermott, Christian Gagné +2
Lipschitz continuity characterizes the worst-case sensitivity of neural networks to small input perturbations; yet its dynamics (i.e. temporal evolution) during training remains un…
Higher-Order Singular-Value Derivatives of Rectangular Real Matrices
Róisín Luo, James McDermott, Colm O'Riordan
We present a theoretical framework for deriving the general -th order Fréchet derivatives of singular values in real rectangular matrices, by leveraging reduced resolvent operat…
Interpreting Global Perturbation Robustness of Image Models using Axiomatic Spectral Importance Decomposition
Róisín Luo, James McDermott, Colm O'Riordan
Perturbation robustness evaluates the vulnerabilities of models, arising from a variety of perturbations, such as data corruptions and adversarial attacks. Understanding the mechan…
Reclaiming Residual Knowledge: A Novel Paradigm to Low-Bit Quantization
Róisín Luo, Alexandru Drimbarean, James McDermott +1
This paper explores a novel paradigm in low-bit (i.e. 4-bits or lower) quantization, differing from existing state-of-the-art methods, by framing optimal quantization as an archite…