1 citations · 2 across the 6 of their papers we have counts for
22 papers
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
Singquandles, Psyquandles and Singular Knots: A Survey
Jose Ceniceros, Indu R. Churchill, Mohamed Elhamdadi +1
In this short survey we review recent results dealing with algebraic structures (quandles, psyquandles, and singquandles) related to singular knot theory. We first explore the sing…
Topological Deep Learning: Classification Neural Networks
Mustafa Hajij, Kyle Istvan
Topological deep learning is a formalism that is aimed at introducing topological language to deep learning for the purpose of utilizing the minimal mathematical structures to form…
Persistent Homology and Graphs Representation Learning
Mustafa Hajij, Ghada Zamzmi, Xuanting Cai
This article aims to study the topological invariant properties encoded in node graph representational embeddings by utilizing tools available in persistent homology. Specifically,…
TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-Ray Images
Mustafa Hajij, Ghada Zamzmi, Fawwaz Batayneh
Topological Data Analysis (TDA) has emerged recently as a robust tool to extract and compare the structure of datasets. TDA identifies features in data such as connected components…
Algebraically-Informed Deep Networks (AIDN): A Deep Learning Approach to Represent Algebraic Structures
Mustafa Hajij, Ghada Zamzmi, Matthew Dawson +1
One of the central problems in the interface of deep learning and mathematics is that of building learning systems that can automatically uncover underlying mathematical laws from…