Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/jimaging7090173 | JOURNAL OF IMAGING |
Keywords | DocType | Volume |
simplicial-map neural networks, artificial neural networks, computational topology | Journal | 7 |
Issue | ISSN | Citations |
9 | 2313-433X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eduardo Paluzo-Hidalgo | 1 | 0 | 1.01 |
| rocio gonzalezdiaz | 2 | 126 | 17.14 |
| Miguel Angel Gutiérrez-Naranjo | 3 | 32 | 3.33 |
| Jónathan Heras | 4 | 94 | 23.31 |