3 citations · 3 across the 2 of their papers we have counts for
3 papers
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…
Forecasting Seasonal Influenza Epidemics with Physics-Informed Neural Networks
Martina Rama, Gabriele Santin, Giulia Cencetti +2
Accurate epidemic forecasting is critical for informing public health decisions and timely interventions. While Physics-Informed Neural Networks have shown promise in various scien…
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…