3 citations · 5 across the 3 of their papers we have counts for
3 papers
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…
EGG-GAE: scalable graph neural networks for tabular data imputation
Lev Telyatnikov, Simone Scardapane
Missing data imputation (MDI) is crucial when dealing with tabular datasets across various domains. Autoencoders can be trained to reconstruct missing values, and graph autoencoder…