7 papers
Topological Neural Operators
Lennart Bastian, Samuel Leventhal, Mustafa Hajij +1
We introduce Topological Neural Operators (TNOs), a principled framework for operator learning on cell complexes that lifts neural operators (NOs) from functions on points and/or e…
TopoU-Net: a U-Net architecture for topological domains
Gaurav Gaurav, Ibrahem ALJabea, Yaroslav Zakomornyy +4
Many modern datasets mix points, edges, regions, groups, objects, events, hyperedges, and relations. Yet neural architectures often force such data into grids, graphs, or sequences…
Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework
Mustafa Hajij, Lennart Bastian, Sarah Osentoski +9
We introduce copresheaf topological neural networks (CTNNs), a powerful unifying framework that encapsulates a wide spectrum of deep learning architectures, designed to operate on…
Topotein: Topological Deep Learning for Protein Representation Learning
Zhiyu Wang, Arian Jamasb, Mustafa Hajij +3
Protein representation learning (PRL) is crucial for understanding structure-function relationships, yet current sequence- and graph-based methods fail to capture the hierarchical…
TopoBench: A Framework for Benchmarking Topological Deep Learning
Lev Telyatnikov, Guillermo Bernardez, Marco Montagna +34
This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL int…
Integrating Project Spatial Coordinates into Pavement Management Prioritization
Shadi Hanandeh, Omar Elbagalati, Mustafa Hajij
To date, pavement management software products and studies on optimizing the prioritization of pavement maintenance and rehabilitation (M&R) have been mainly focused on three param…