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.LG2025
Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves
Alessio Borgi, Fabrizio Silvestri, Pietro Liò
Sheaf Neural Networks equip graph structures with a cellular sheaf: a geometric structure which assigns local vector spaces (stalks) and a linear learnable restriction/transport ma…
cs.LG2025
Symmetry and Generalisation in Neural Approximations of Renormalisation Transformations
Cassidy Ashworth, Pietro Liò, Francesco Caso
Deep learning models have proven enormously successful at using multiple layers of representation to learn relevant features of structured data. Encoding physical symmetries into t…