Marco Pacini
PhD Candidate at University of Trento & Fondazione Bruno Kessler
My research focuses on the fundamental principles of Geometric Deep Learning and Equivariant Machine Learning.
Some of my research interests include the constructive characterization of equivariant models, as well as their expressivity and approximation capabilities.
News
| Feb 11, 2025 | Our latest paper, “Separation Power of Equivariant Neural Networks,” has been accepted to ICLR 2025! ✨ |
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| Jun 14, 2024 | New preprint of “Separation Power of Equivariant Neural Networks” is out on ArXiv now! 🎉 |
| Jan 15, 2024 | Our latest paper “A Characterization Theorem for Equivariant Networks with Point-wise Activations” has been accepted at ICLR 2024! 📘 |
