2 citations · 2 across the 5 of their papers we have counts for
5 papers
Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learning
Mathilde Papillon, Guillermo Bernárdez, Álvaro Ballón Barreiro +4
Topological deep learning (TDL) methods rely on lifting raw data into higher-order discrete domains such as simplicial complexes, cell complexes, and hypergraphs. In practice, this…
HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations
Guillermo Bernárdez, Marco Montagna, Louis Van Langendonck +7
While Graph Neural Networks (GNNs) have proven highly effective at modeling relational data, pairwise connections cannot fully capture multi-way relationships naturally present in…
Topological Deep Learning with State-Space Models: A Mamba Approach for Simplicial Complexes
Marco Montagna, Simone Scardapane, Lev Telyatnikov
Graph Neural Networks based on the message-passing (MP) mechanism are a dominant approach for handling graph-structured data. However, they are inherently limited to modeling only…
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…
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…