Rubén Ballester, Ernst Röell, Daniel Bin Schmid, Mathieu Alain, Sergio Escalera, Carles Casacuberta, Bastian Rieck
MANTRA: The Manifold Triangulations Assemblage
arXiv:2410.02392 (October 2024)
Rubén Ballester, Bastian Rieck
On the expressivity of persistent homology in graph learning
arXiv:2302.09826 (June 2024)
Rubén Ballester, Pablo Hernández-García, Mathilde Papillon, Claudio Battiloro, Nina Miolane, Tolga Birdal, Carles Casacuberta, Sergio Escalera, Mustafa Hajij
Attending to topological spaces: The Cellular Transformer
arXiv:2405.14094 (May 2024)
Rubén Ballester, Carles Casacuberta, Sergio Escalera
Topological data analysis for neural network analysis: A comprehensive survey
arXiv:2312.05840 (December 2023)
Rubén Ballester, Xavier Arnal Clemente, Carles Casacuberta, Meysam Madadi, Ciprian A. Corneanu, Sergio Escalera
Predicting the generalization gap in neural networks using topological data analysis
Neurocomputing 596 (2024), 127787, open access
DOI:10.1016/j.neucom.2024.127787
Rubén Ballester, Carles Casacuberta, Sergio Escalera
Decorrelating neurons using persistence
NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations
Proceedings of Machine Learning Research 228:164-182 (2024), open access
https://proceedings.mlr.press/v228/ballester24a.html
Ferrà A, Cecchini G, Nobbe Fisas F-P, Casacuberta C, Cos I
A topological classifier to characterize brain states: When shape matters more than variance
PLoS ONE 18(10): e0292049 (2023), open access
DOI:10.1371/journal.pone.0292049
Ferrà, A., Casacuberta, C., Pujol, O.
Importance attribution in neural networks by means of persistence landscapes of time series
Neural Computing and Applications 35, 20143-20156 (2023), open access
DOI:10.1007/s00521-023-08731-6
Aina Ferrà, Carles Casacuberta, Oriol Pujol
Reconstruction of univariate functions from directional persistence diagrams
arXiv:2203.01894 (v1: March 2022; v2: February 2023)