4 papers
Persistent Entropy Transform: An entropy-based descriptor for topological data analysis
Victor Toscano-Duran, Miguel A. Gutiérrez Naranjo, Rocio Gonzalez-Diaz
Persistent entropy provides a compact summary of persistence diagrams, but discards geometric information inherent to the data. This limitation creates a gap between scalar summari…
Barycentric Neural Networks and Length-Weighted Persistent Entropy Loss: A Green Geometric and Topological Framework for Function Approximation
Victor Toscano-Duran, Rocio Gonzalez-Diaz, Miguel A. Gutiérrez-Naranjo
While artificial neural networks are known as universal approximators for continuous functions, many modern approaches rely on overparameterized architectures with high computation…
An In-Depth Analysis of Data Reduction Methods for Sustainable Deep Learning
Víctor Toscano-Durán, Javier Perera-Lago, Eduardo Paluzo-Hidalgo +3
In recent years, Deep Learning has gained popularity for its ability to solve complex classification tasks, increasingly delivering better results thanks to the development of more…
SIMAP: A simplicial-map layer for neural networks
Rocio Gonzalez-Diaz, Miguel A. Gutiérrez-Naranjo, Eduardo Paluzo-Hidalgo
In this paper, we present SIMAP, a novel layer integrated into deep learning models, aimed at enhancing the interpretability of the output. The SIMAP layer is an enhanced version o…