works on

From the 1 of 5 linked papers with an AI index.

most citedExplainable Autoencoder-Based Anomaly Detection in IEC 61850 GOOSE Networks

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts

Malena Loza, Felipe Grijalva, Eva Milara +3

Tabular-to-image methods that convert tabular data into visual representations have emerged as a novel paradigm for leveraging the high performance of deep learning models. Despite…

cs.LG2026

Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models

Malena Loza, David Chushig-Muzo, Eva Milara +3

The paper empirically evaluates how nine tabular foundation models perform under various out-of-distribution shifts using real-world datasets, finding systematic performance degrad…

cs.CR20261 cited

Explainable Autoencoder-Based Anomaly Detection in IEC 61850 GOOSE Networks

Dafne Lozano-Paredes, Luis Bote-Curiel, Juan Ramón Feijóo-Martínez +2

The IEC 61850 Generic Object-Oriented Substation Event (GOOSE) protocol plays a critical role in real-time protection and automation of digital substations, yet its lack of native…

cs.CY2025

Understanding the Disparities in Mathematics Performance: An Interpretability-Based Examination

Ismael Gomez-Talal, Luis Bote-Curiel, Jose Luis Rojo-Alvarez

Problem. Educational disparities in Mathematics performance are a persistent challenge. This study aims to unravel the complex factors contributing to these disparities among stude…

cs.LG2025

Multivariate Feature Selection and Autoencoder Embeddings of Ovarian Cancer Clinical and Genetic Data

Luis Bote-Curiel, Sergio Ruiz-Llorente, Sergio Muñoz-Romero +4

This study explores a data-driven approach to discovering novel clinical and genetic markers in ovarian cancer (OC). Two main analyses were performed: (1) a nonlinear examination o…