Representative datasets for neural networks. - Rocío González-Díaz; Eduardo Paluzo-Hidalgo; Miguel Angel Gutiérrez-Naranjo (Electron. Notes Discret. Math., 2018)

Towards Emotion Recognition: A Persistent Entropy Application. - Rocío González-Díaz; Eduardo Paluzo-Hidalgo; José F. Quesada 001 (CTIC, 2019)

Representative Datasets: The Perceptron Case. - Rocío González-Díaz; Miguel Angel Gutiérrez-Naranjo; Eduardo Paluzo-Hidalgo (CoRR, 2019)

Two-hidden-layer Feedforward Neural Networks are Universal Approximators: A Constructive Approach. - Rocío González-Díaz; Miguel Angel Gutiérrez-Naranjo; Eduardo Paluzo-Hidalgo (CoRR, 2019)

Towards a Philological Metric through a Topological Data Analysis Approach. - Eduardo Paluzo-Hidalgo; Rocío González-Díaz; Miguel Angel Gutiérrez-Naranjo (CoRR, 2019)

Euler Well-Composedness. - Nicolas Boutry; Rocío González-Díaz; María José Jiménez 001; Eduardo Paluzo-Hidalgo (IWCIA, 2020)

Two-hidden-layer feed-forward networks are universal approximators: A constructive approach. - Eduardo Paluzo-Hidalgo; Rocío González-Díaz; Miguel Angel Gutiérrez-Naranjo (Neural Networks, 2020)

Approximating lower-star persistence via 2D combinatorial map simplification. - Guillaume Damiand; Eduardo Paluzo-Hidalgo; Ryan Slechta; Rocío González-Díaz (Pattern Recognit. Lett., 2020)

Emotion recognition in talking-face videos using persistent entropy and neural networks. - Eduardo Paluzo-Hidalgo; Guillermo Aguirre-Carrazana; Rocío González-Díaz (CoRR, 2021)

Optimizing the Simplicial-Map Neural Network Architecture. - Eduardo Paluzo-Hidalgo; Rocío González-Díaz; Miguel Angel Gutiérrez-Naranjo; Jónathan Heras (J. Imaging, 2021)

Strong Euler well-composedness. - Nicolas Boutry; Rocío González-Díaz; María José Jiménez 001; Eduardo Paluzo-Hidalgo (J. Comb. Optim., 2022)

Topology-based representative datasets to reduce neural network training resources. - Rocío González-Díaz; Miguel Angel Gutiérrez-Naranjo; Eduardo Paluzo-Hidalgo (Neural Comput. Appl., 2022)

The Metric-aware Kernel-width Choice for LIME. - Aurelio Barrera-Vicent; Eduardo Paluzo-Hidalgo; Miguel Angel Gutiérrez-Naranjo (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2023)

Explainability in Simplicial Map Neural Networks. - Eduardo Paluzo-Hidalgo; Miguel Angel Gutiérrez-Naranjo; Rocío González-Díaz (CoRR, 2023)

A Topological Approach to Measuring Training Data Quality. - Álvaro Torras-Casas; Eduardo Paluzo-Hidalgo; Rocío González-Díaz (CoRR, 2023)

A Survey of Vectorization Methods in Topological Data Analysis. - Dashti Ali; Aras Asaad; María José Jiménez 001; Vidit Nanda; Eduardo Paluzo-Hidalgo; Manuel Soriano-Trigueros (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Application of the Representative Measure Approach to Assess the Reliability of Decision Trees in Dealing with Unseen Vehicle Collision Data. - Javier Perera-Lago; Víctor Toscano-Durán; Eduardo Paluzo-Hidalgo; Sara Narteni; Matteo Rucco (xAI (4), 2024)

SIMAP: A simplicial-map layer for neural networks. - Rocío González-Díaz; Miguel Angel Gutiérrez-Naranjo; Eduardo Paluzo-Hidalgo (CoRR, 2024)

An In-Depth Analysis of Data Reduction Methods for Sustainable Deep Learning. - Víctor Toscano-Durán; Javier Perera-Lago; Eduardo Paluzo-Hidalgo; Rocío González-Díaz; Miguel Angel Gutiérrez-Naranjo; Matteo Rucco (CoRR, 2024)

Trainable and explainable simplicial map neural networks. - Eduardo Paluzo-Hidalgo; Rocío González-Díaz; Miguel Angel Gutiérrez-Naranjo (Inf. Sci., 2024)

Latent Space Topology Evolution in Multilayer Perceptrons. - Eduardo Paluzo-Hidalgo (CoRR, 2025)