Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Katende, Ronald"'
Autor:
Katende, Ronald
Structural transformation, the shift from agrarian economies to more diversified industrial and service-based systems, is a key driver of economic development. However, in low- and middle-income countries (LMICs), data scarcity and unreliability hind
Externí odkaz:
http://arxiv.org/abs/2409.16738
Autor:
Katende, Ronald, Kasumba, Henry
This paper addresses the challenges of high-dimensional non-convex optimization, particularly the inefficiencies caused by saddle points. The authors propose several techniques for detecting, evading, and optimizing in the presence of these saddle po
Externí odkaz:
http://arxiv.org/abs/2409.12604
Autor:
Katende, Ronald
This manuscript presents a novel framework that integrates higher-order symmetries and category theory into machine learning. We introduce new mathematical constructs, including hyper-symmetry categories and functorial representations, to model compl
Externí odkaz:
http://arxiv.org/abs/2409.12100
Autor:
Katende, Ronald
Nonlinear eigenvalue problems (NEPs) present significant challenges due to their inherent complexity and the limitations of traditional linear eigenvalue theory. This paper addresses these challenges by introducing a nonlinear generalization of the B
Externí odkaz:
http://arxiv.org/abs/2409.11098
Autor:
Katende, Ronald
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 approaches to tensor eigenvalue analysis often extend matrix theory,
Externí odkaz:
http://arxiv.org/abs/2409.09392
Autor:
Katende, Ronald
Climate resilience across sectors varies significantly in low-income countries (LICs), with agriculture being the most vulnerable to climate change. Existing studies typically focus on individual countries, offering limited insights into broader cros
Externí odkaz:
http://arxiv.org/abs/2409.08765
Autor:
Katende, Ronald
This paper explores the integration of Diophantine equations into neural network (NN) architectures to improve model interpretability, stability, and efficiency. By encoding and decoding neural network parameters as integer solutions to Diophantine e
Externí odkaz:
http://arxiv.org/abs/2409.07310
Autor:
Mango, John, Katende, Ronald
This paper introduces a dynamic, error-bounded hierarchical matrix (H-matrix) compression method tailored for Physics-Informed Neural Networks (PINNs). The proposed approach reduces the computational complexity and memory demands of large-scale physi
Externí odkaz:
http://arxiv.org/abs/2409.07028
Autor:
Katende, Ronald
This paper introduces a novel method for eigenvalue computation using a distributed cooperative neural network framework. Unlike traditional techniques that face scalability challenges in large systems, our decentralized algorithm enables multiple au
Externí odkaz:
http://arxiv.org/abs/2409.06746
Autor:
Katende, Ronald
In this paper, we introduce a novel matrix decomposition method, referred to as the \( D \)-decomposition, designed to improve computational efficiency and stability for solving high-dimensional linear systems. The decomposition factorizes a matrix \
Externí odkaz:
http://arxiv.org/abs/2409.06321