2 papers
cs.LG2025
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…
stat.ML2025
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…