Work

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

Year: 2025

Type: article

Abstract: 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.

Source: npj Artificial Intelligence

DOI: 10.1038/s44387-025-00034-6

Authors: Paolo Branchini, Pierluigi Contucci, Sacha Cormenier, Gianluca Manzan

Institutions: Istituto Nazionale di Fisica Nucleare, Sezione di Roma Tre, University of Bologna, Roma Tre University

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,590

OpenAlex ID: W4415992859