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cs.LG2025
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…
cs.LG2025
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…
cs.LG2025
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…