3 papers
cs.LG2026
Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs
Alessio Borgi, Mario Severino, Fabrizio Silvestri +1
Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-order architectures remain limited in how vector information can be trans…
cs.LG2026
Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces
Mario Severino, Manuela Moretto, Robert A. McCutcheon +1
Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the curved geometry of correlation sp…
cs.LG2026
Remember to Forget: Gated Adaptive Positional Encoding
Riccardo Ali, Alessio Borgi, Christopher Irwin +2
Rotary Positional Encoding (RoPE) is widely used in modern large language models. However, when sequences are extended beyond the range seen during training, rotary phases can ente…