activity
20242026
collaborators

6 papers

eess.IV2026

Deep Slice Interpolation for Reducing Through-Plane Anisotropy and Noise in Head CT

Luis Cortés Ferre, Miguel A. Gutiérrez-Naranjo, Marcin Balcerzyk

Head computed tomography (CT) typically uses sub-millimeter in-plane resolution but 2-5 mm through-plane spacing, creating substantial anisotropy that degrades multiplanar reconstr…

cs.LG2026

Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance

Elvin Somón, Miguel A. Gutiérrez-Naranjo

We conducted a reproducibility-oriented re-evaluation of prior migraine classification studies, correcting for data leakage and metric bias. We then introduced (i) a clinically mot…

cs.CL2026

Interpretability of the Intent Detection Problem: A New Approach

Eduardo Sanchez-Karhunen, Jose F. Quesada-Moreno, Miguel A. Gutiérrez-Naranjo

Intent detection, a fundamental text classification task, aims to identify and label the semantics of user queries, playing a vital role in numerous business applications. Despite…

cs.LG2025

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…

cs.LG2024

Interpretation of the Intent Detection Problem as Dynamics in a Low-dimensional Space

Eduardo Sanchez-Karhunen, Jose F. Quesada-Moreno, Miguel A. Gutiérrez-Naranjo

Intent detection is a text classification task whose aim is to recognize and label the semantics behind a users query. It plays a critical role in various business applications. Th…

cs.GT2024

A Membrane Computing Approach to the Generalized Nash Equilibrium

Alejandro Luque-Cerpa, Miguel A. Gutiérrez-Naranjo

In Evolutionary Game Theory (EGT), a population reaches a Nash equilibrium when none of the agents can improve its objective by solely changing its strategy on its own. Roughly spe…