Search Results for author: Aldo Guzmán-Sáenz

Found 6 papers, 4 papers with code

Topo-MLP : A Simplicial Network Without Message Passing

no code implementations19 Dec 2023 Karthikeyan Natesan Ramamurthy, Aldo Guzmán-Sáenz, Mustafa Hajij

To overcome such limitations, we propose Topo-MLP, a purely MLP-based simplicial neural network algorithm to learn the representation of elements in a simplicial complex without explicitly relying on message passing.

Representation Learning

Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs

no code implementations15 Dec 2023 Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Aldo Guzmán-Sáenz, Tolga Birdal, Michael T. Schaub

In this context, cell complexes are often seen as a subclass of hypergraphs with additional algebraic structure that can be exploited, e. g., to develop a spectral theory.

Probing omics data via harmonic persistent homology

1 code implementation10 Nov 2023 Davide Gurnari, Aldo Guzmán-Sáenz, Filippo Utro, Aritra Bose, Saugata Basu, Laxmi Parida

Identifying molecular signatures from complex disease patients with underlying symptomatic similarities is a significant challenge in analysis of high dimensional multi-omics data.

Topological Data Analysis

Topological Deep Learning: Going Beyond Graph Data

3 code implementations1 Jun 2022 Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K. Dey, Soham Mukherjee, Shreyas N. Samaga, Neal Livesay, Robin Walters, Paul Rosen, Michael T. Schaub

Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes, cell complexes, and hypergraphs, which generalize many domains encountered in scientific computations.

Graph Learning

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