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Separation Power of Equivariant Neural Networks

Marco Pacini, Xiaowen Dong, Bruno Lepri, Gabriele Santin
Jun 5, 2026 at 04:00
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arXiv:2406.08966v3 Announce Type: replace-cross Abstract: 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 the separation power of a family of models is a necessary condition to obtain fine-grained universality results....

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