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Deep Neural Networks via Complex Network Theory: a Perspective
April 19, 2024, 4:42 a.m. | Emanuele La Malfa, Gabriele La Malfa, Giuseppe Nicosia, Vito Latora
cs.LG updates on arXiv.org arxiv.org
Abstract: Deep Neural Networks (DNNs) can be represented as graphs whose links and vertices iteratively process data and solve tasks sub-optimally. Complex Network Theory (CNT), merging statistical physics with graph theory, provides a method for interpreting neural networks by analysing their weights and neuron structures. However, classic works adapt CNT metrics that only permit a topological analysis as they do not account for the effect of the input data. In addition, CNT metrics have been applied …
abstract arxiv cs.ai cs.lg data graph graphs however merging network networks neural networks neuron perspective physics process solve statistical tasks theory type via
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