6 papers
Graph Hierarchical Recurrence for Long-Range Generalization
Stefano Carotti, Marco Pacini, Alessio Gravina +3
Graph Neural Networks and Graph Transformers have become central to graph learning, combining expressive representation learning with sample-efficient inductive biases. Yet they re…
On Uncertainty Calibration for Equivariant Functions
Edward Berman, Jacob Ginesin, Marco Pacini +1
Data-sparse settings such as robotic manipulation, molecular physics, and galaxy morphology classification are some of the hardest domains for deep learning. For these problems, eq…
On Universality of Deep Equivariant Networks
Marco Pacini, Mircea Petrache, Bruno Lepri +2
Universality results for equivariant neural networks remain rare. Those that do exist typically hold only in restrictive settings: either they rely on regular or higher-order tenso…
On Universality Classes of Equivariant Networks
Marco Pacini, Gabriele Santin, Bruno Lepri +1
Equivariant neural networks provide a principled framework for incorporating symmetry into learning architectures and have been extensively analyzed through the lens of their separ…
Separation Power of Equivariant Neural Networks
Marco Pacini, Xiaowen Dong, Bruno Lepri +1
The separation power of a machine learning model refers to its ability to distinguish between different inputs and is often used as a proxy for its expressivity. Indeed, knowing th…
A Characterization Theorem for Equivariant Networks with Point-wise Activations
Marco Pacini, Xiaowen Dong, Bruno Lepri +1
Equivariant neural networks have shown improved performance, expressiveness and sample complexity on symmetrical domains. But for some specific symmetries, representations, and cho…