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Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations
April 26, 2024, 4:42 a.m. | Elia Cunegatti, Matteo Farina, Doina Bucur, Giovanni Iacca
cs.LG updates on arXiv.org arxiv.org
Abstract: Pruning-at-Initialization (PaI) algorithms provide Sparse Neural Networks (SNNs) which are computationally more efficient than their dense counterparts, and try to avoid performance degradation. While much emphasis has been directed towards \emph{how} to prune, we still do not know \emph{what topological metrics} of the SNNs characterize \emph{good performance}. From prior work, we have layer-wise topological metrics by which SNN performance can be predicted: the Ramanujan-based metrics. To exploit these metrics, proper ways to represent network layers …
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