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<p>We study how to optimise the architecture of a Deep Neural Network by rearranging the neurons within the hidden layers. In order to do so, we follow a theoretical suggestion that comes from the thermodynamic properties of some Restricted Boltzmann machines (RBMs). Namely, for a suitable definition of temperature, we move the neurons towards colder areas of the network. We find that such a procedure, in the analysed cases, improves the robustness of the network respectively by 4.8%, 6% and 2.8% with respect to the default choice of neurons equidistribution, without changing the accuracy. The result has a particular relevance in AI applications to satellites where resources are limited.</p>

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Architectural optimisation in deep neural networks. Tests of a theoretically inspired method

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Paolo Branchini,
  • Pierluigi Contucci,
  • Sacha Cormenier,
  • Gianluca Manzan

摘要

We study how to optimise the architecture of a Deep Neural Network by rearranging the neurons within the hidden layers. In order to do so, we follow a theoretical suggestion that comes from the thermodynamic properties of some Restricted Boltzmann machines (RBMs). Namely, for a suitable definition of temperature, we move the neurons towards colder areas of the network. We find that such a procedure, in the analysed cases, improves the robustness of the network respectively by 4.8%, 6% and 2.8% with respect to the default choice of neurons equidistribution, without changing the accuracy. The result has a particular relevance in AI applications to satellites where resources are limited.