7 citations · 13 across the 10 of their papers we have counts for
10 papers
Learning Tangent Bundles and Characteristic Classes with Autoencoder Atlases
Eduardo Paluzo-Hidalgo, Yuichi Ike
We introduce a theoretical framework that connects multi-chart autoencoders in manifold learning with the classical theory of vector bundles and characteristic classes. Rather than…
Persistent Entropy as a Detector of Phase Transitions
Marcos Gutierrez-del-Pozo, Eduardo Paluzo-Hidalgo, Matteo Rucco
Persistent entropy is a scalar summary of persistence barcodes widely used to detect regime changes, yet there is no account of when a structural change in a barcode must produce a…
Latent Space Topology Evolution in Multilayer Perceptrons
Eduardo Paluzo-Hidalgo
This paper introduces a topological framework for interpreting the internal representations of Multilayer Perceptrons (MLPs). We construct a simplicial tower, a sequence of simplic…
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 +2
Machine learning algorithms are fundamental components of novel data-informed Artificial Intelligence architecture. In this domain, the imperative role of representative datasets i…
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…
Topological Quality of Subsets via Persistence Matching Diagrams
Álvaro Torras-Casas, Eduardo Paluzo-Hidalgo, Rocio Gonzalez-Diaz
Data quality is crucial for the successful training, generalization and performance of machine learning models. We propose to measure the quality of a subset concerning the dataset…
Trainable and Explainable Simplicial Map Neural Networks
Eduardo Paluzo-Hidalgo, Miguel A. Gutiérrez-Naranjo, Rocio Gonzalez-Diaz
Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation ability and robustness to adversarial examples…
Emotion recognition in talking-face videos using persistent entropy and neural networks
Eduardo Paluzo-Hidalgo, Guillermo Aguirre-Carrazana, Rocio Gonzalez-Diaz
The automatic recognition of a person's emotional state has become a very active research field that involves scientists specialized in different areas such as artificial intellige…
Topology-based Representative Datasets to Reduce Neural Network Training Resources
Rocio Gonzalez-Diaz, Miguel A. Gutiérrez-Naranjo, Eduardo Paluzo-Hidalgo
One of the main drawbacks of the practical use of neural networks is the long time required in the training process. Such a training process consists of an iterative change of para…