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Marco Pacini

Researcher at Fondazione Bruno Kessler, Trento, Italy

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.

Selected Publications

Approximation Theory of Equivariant Neural Networks

  1. ICLR
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    On Universality of Deep Equivariant Networks
    Marco Pacini, Mircea Petrache, Bruno Lepri, and 2 more authors
    International Conference on Learning Representations, 2026
  2. NeurIPS
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    On Universality Classes of Equivariant Networks
    Marco Pacini, Gabriele Santin, Bruno Lepri, and 1 more author
    Conference on Neural Information Processing Systems, 2025
  3. ICLR
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    Separation Power of Equivariant Neural Networks
    Marco Pacini, Xiaowen Dong, Bruno Lepri, and 1 more author
    International Conference on Learning Representations, 2025

Characterization of Equivariant Activations

  1. ICLR
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    A Characterization Theorem for Equivariant Networks with Point-wise Activations
    Marco Pacini, Xiaowen Dong, Bruno Lepri, and 1 more author
    International Conference on Learning Representations, 2024