3 citations · 4 across the 13 of their papers we have counts for
7 papers · 1 filter
Decentralized Neural Networks for Robust and Scalable Eigenvalue Computation
Ronald Katende
This paper introduces a novel method for eigenvalue computation using a distributed cooperative neural network framework. Unlike traditional techniques that face scalability challe…
Symmetry-Enriched Learning: A Category-Theoretic Framework for Robust Machine Learning Models
Ronald Katende
This manuscript presents a novel framework that integrates higher-order symmetries and category theory into machine learning. We introduce new mathematical constructs, including hy…
A Nonlinear Generalization of the Bauer-Fike Theorem and Novel Iterative Methods for Solving Nonlinear Eigenvalue Problems
Ronald Katende
Nonlinear eigenvalue problems (NEPs) present significant challenges due to their inherent complexity and the limitations of traditional linear eigenvalue theory. This paper address…
Cross-Country Comparative Analysis of Climate Resilience and Localized Mapping in Data-Sparse Regions
Ronald Katende
Climate resilience across sectors varies significantly in low-income countries (LICs), with agriculture being the most vulnerable to climate change. Existing studies typically focu…
Optimizing Neural Network Performance and Interpretability with Diophantine Equation Encoding
Ronald Katende
This paper explores the integration of Diophantine equations into neural network (NN) architectures to improve model interpretability, stability, and efficiency. By encoding and de…
Topological Eigenvalue Theorems for Tensor Analysis in Multi-Modal Data Fusion
Ronald Katende
This paper presents a novel framework for tensor eigenvalue analysis in the context of multi-modal data fusion, leveraging topological invariants such as Betti numbers. Traditional…
Core-Conditioned Regularized Matrix Tri-Factorization for High-Dimensional Structured Systems
Ronald Katende
This paper studies a regularized matrix tri-factorization \(A\approx PDQ\), where \(P\) and \(Q\) are side factors and \(D\) is a central core whose conditioning can be explicitly…