1 citations · 1 across the 7 of their papers we have counts for
7 papers
A Function-Space Stability Boundary for Generalization in Interpolating Learning Systems
Ronald Katende
Modern learning systems often interpolate training data while still generalizing well, yet it remains unclear when algorithmic stability explains this behavior. We model training a…
A Unified Matrix-Spectral Framework for Stability and Interpretability in Deep Learning
Ronald Katende
We develop a unified matrix-spectral framework for analyzing stability and interpretability in deep neural networks. Representing networks as data-dependent products of linear oper…
Why Smooth Stability Assumptions Fail for ReLU Learning
Ronald Katende
Stability analyses of modern learning systems are frequently derived under smoothness assumptions that are violated by ReLU-type nonlinearities. In this note, we isolate a minimal…
A Learning Stability Profile for Finite-Dimensional Learning Dynamics
Ronald Katende
We develop a finite-dimensional sensitivity framework for studying stability in learning systems whose states include representations, parameters, and update variables. The central…
Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis
Ronald Katende
We present a unified theoretical framework for analyzing the stability and consistency of Physics-Informed Neural Networks (PINNs), grounded in operator coercivity, variational for…
Strange Attractors in Fractional Differential Equations: A Topological Approach to Chaos and Stability
Ronald Katende
In this work, we explore the dynamics of fractional differential equations (FDEs) through a rigorous topological analysis of strange attractors. By investigating systems with Caput…
Unified theoretical guarantees for stability, consistency, and convergence in neural PDE solvers from non-IID data to physics-informed networks
Ronald Katende
We establish a unified theoretical framework addressing the stability, consistency, and convergence of neural networks under realistic training conditions, specifically, in the pre…