36 research outputs found
Optimizing the Simplicial-Map Neural Network Architecture
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.Agencia Estatal de Investigaci贸n PID2019-107339GB-10