| _version_ | 1866901268801257472 |
|---|---|
| author | Ben-Hur Varriano |
| author_facet | Ben-Hur Varriano |
| contents | <p>In this work, we present a detailed mathematical description of a novel multi-layer network architecture called <strong>HurNet</strong>. This model is designed as a lightweight alternative to traditional deep learning frameworks, focusing on computational efficiency and simplified training processes. We derive the mathematical foundations behind the core modules, including input validation, activation function formulation, proximity-based weight selection, and performance metrics. The methodology is supported by a wide array of equations and analytical proofs, and our work is contextualized with references from seminal works in neural computation and optimization. The results suggest that <strong>HurNet</strong> achieves similar accuracy to conventional frameworks while reducing computational time significantly.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19645833 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Novel Multi-Layer HurNet Architecture: A Fast Alternative to Traditional Deep Neural Networks Ben-Hur Varriano <p>In this work, we present a detailed mathematical description of a novel multi-layer network architecture called <strong>HurNet</strong>. This model is designed as a lightweight alternative to traditional deep learning frameworks, focusing on computational efficiency and simplified training processes. We derive the mathematical foundations behind the core modules, including input validation, activation function formulation, proximity-based weight selection, and performance metrics. The methodology is supported by a wide array of equations and analytical proofs, and our work is contextualized with references from seminal works in neural computation and optimization. The results suggest that <strong>HurNet</strong> achieves similar accuracy to conventional frameworks while reducing computational time significantly.</p> |
| title | A Novel Multi-Layer HurNet Architecture: A Fast Alternative to Traditional Deep Neural Networks |
| url | https://doi.org/10.5281/zenodo.19645833 |