3 citations · 3 across the 4 of their papers we have counts for
5 papers
VG-TIE: An interpretable tabular-to-image encoding method based on visibility graphs
David Chushig-Muzo, Luis M. López-Ramos, Ángeles Rodríguez de Cara +3
Tabular-to-image encoding methods enable the application of models based on both convolutional neural networks and vision transformers to tabular data, transforming feature vectors…
TabSOM: A tabular-to-image encoding method based on self-organizing maps
David Chushig-Muzo, María Ángeles Rodríguez de Cara, Eva Milara +3
Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers. They convert tabular…
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…
Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models
Malena Loza, David Chushig-Muzo, Eva Milara +3
Tabular Foundation Models (TFMs) have emerged as novel approaches for tabular predictive tasks, demonstrating competitive predictive performance to ensemble tree-based models. Most…
LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks
Vanesa Gómez-Martínez, Francisco J. Lara-Abelenda, Pablo Peiro-Corbacho +3
Tabular data have been extensively used in different knowledge domains. Convolutional neural networks (CNNs) have been successfully used in many applications where important inform…