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Network Features in AI Applications

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  • UserLucia Cavallaro (Radboud Universiteit Nijmegen)
  • ClockTuesday 13 August 2024, 11:00-12:00
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HTA - Hypergraphs: Theory and Applications

This talk shows how Network Science can be applied to miniaturize Artificial Neural Networks (ANNs). More specifically, an efficient sparsification strategy using Erdős-Rényi random networks to streamline the training of ANNs is described. Initial results are promising, with Sparse ANNs significantly reduced in size (more than a 75% of sparsity) while experiencing minimal accuracy loss. Future research will explore using Network Science techniques to gain insights into how ANNs operate.  

This talk is part of the Isaac Newton Institute Seminar Series series.

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