2 citations · 5 across the 7 of their papers we have counts for
8 papers
Motif-based filtrations for persistent homology: A framework for graph isomorphism and property prediction
Meritxell Vila-Miñana, Robert Jankowski, Aina Ferrà Marcús +3
Determining whether two graphs are isomorphic is a fundamental problem with practical applications in areas such as molecular chemistry or social network analysis, yet it remains a…
MANTRA: The Manifold Triangulations Assemblage
Rubén Ballester, Ernst Röell, Daniel Bīn Schmid +4
The rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting higher-order structures in the data,…
Attending to Topological Spaces: The Cellular Transformer
Rubén Ballester, Pablo Hernández-García, Mathilde Papillon +6
Topological Deep Learning seeks to enhance the predictive performance of neural network models by harnessing topological structures in input data. Topological neural networks opera…
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
Mustafa Hajij, Mathilde Papillon, Florian Frantzen +40
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: h…
Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey
Rubén Ballester, Carles Casacuberta, Sergio Escalera
This survey provides a comprehensive exploration of applications of Topological Data Analysis (TDA) within neural network analysis. Using TDA tools such as persistent homology and…
ICML 2023 Topological Deep Learning Challenge : Design and Results
Mathilde Papillon, Mustafa Hajij, Helen Jenne +53
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competi…
Decorrelating neurons using persistence
Rubén Ballester, Carles Casacuberta, Sergio Escalera
We propose a novel way to improve the generalisation capacity of deep learning models by reducing high correlations between neurons. For this, we present two regularisation terms c…
Predicting the generalization gap in neural networks using topological data analysis
Rubén Ballester, Xavier Arnal Clemente, Carles Casacuberta +3
Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models. In this paper, we study the generalization gap of neural netwo…