Artículo
Optimizing the Simplicial-Map Neural Network Architecture
Autor/es | Paluzo Hidalgo, Eduardo
González Díaz, Rocío Gutiérrez Naranjo, Miguel Ángel Heras, Jónathan |
Departamento | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2021 |
Fecha de depósito | 2021-10-20 |
Publicado en |
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Resumen | Simplicial-map neural networks are a recent neural network architecture induced by
simplicial maps defined between simplicial complexes. It has been proved that simplicial-map neural
networks are universal approximators ... Simplicial-map neural networks are a recent neural network architecture induced by simplicial maps defined between simplicial complexes. It has been proved that simplicial-map neural networks are universal approximators and that they can be refined to be robust to adversarial attacks. In this paper, the refinement toward robustness is optimized by reducing the number of simplices (i.e., nodes) needed. We have shown experimentally that such a refined neural network is equivalent to the original network as a classification tool but requires much less storage. |
Agencias financiadoras | Agencia Estatal de Investigación. España |
Identificador del proyecto | PID2019- 107339GB-100 |
Cita | Paluzo Hidalgo, E., González Díaz, R., Gutiérrez Naranjo, M.Á. y Heras, J. (2021). Optimizing the Simplicial-Map Neural Network Architecture. Journal of Imaging, 7 (9) |
Ficheros | Tamaño | Formato | Ver | Descripción |
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jimaging-07-00173.pdf | 1.671Mb | [PDF] | Ver/ | |