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Discovering Wiring Patterns Influencing Neural Network Performance

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13715))

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Abstract

The search for optimal neural network architecture is a well-known problem in deep learning. However, as many algorithms have been proposed in this domain, little attention is given to the analysis of wiring properties that are beneficial or detrimental to the network performance. We take a step at addressing this issue by performing a massive evaluation of artificial neural networks with various computational architectures, where the diversity of the studied constructions is obtained by basing the wiring topology of the networks on different types of random graphs. Our goal is to investigate the structural and numerical properties of the graphs and assess their relation to the test accuracy of the corresponding neural networks. We find that none of the classical numerical graph invariants by itself allows to single out the best networks. Consequently, we introduce a new numerical graph characteristic, called quasi-1-dimensionality, which is able to identify the majority of the best-performing graphs.

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Notes

  1. 1.

    The code is available at https://github.com/rmldj/random-graph-nn-paper.

  2. 2.

    Refer to Appendix I for a full list.

  3. 3.

    We provide a full description of the training procedure in Appendix A.

  4. 4.

    See a visualization of a bottleneck graph in Appendix I.

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Acknowledgments

Work carried out within the research project Bio-inspired artificial neural network (grant no. POIR.04.04.00-00-14DE/18-00) within the Team-Net program of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund. The fMRI data were provided by the Human Connectome Project, WU-Minn Consortium (PIs: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348, Kraków, Poland

    Aleksandra I. Nowak

  2. Institute of Theoretical Physics, Jagiellonian University, Łojasiewicza 11, 30-348, Kraków, Poland

    Romuald A. Janik

Authors
  1. Aleksandra I. Nowak
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  2. Romuald A. Janik
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Corresponding author

Correspondence to Aleksandra I. Nowak .

Editor information

Editors and Affiliations

  1. Grenoble Alpes University, Saint Martin d'Hères, France

    Massih-Reza Amini

  2. INSA Rouen Normandy, Saint Etienne du Rouvray, France

    Stéphane Canu

  3. Ruhr-Universität Bochum, Bochum, Germany

    Asja Fischer

  4. KU Leuven, Leuven, Belgium

    Tias Guns

  5. Central European University, Vienna, Austria

    Petra Kralj Novak

  6. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

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Nowak, A.I., Janik, R.A. (2023). Discovering Wiring Patterns Influencing Neural Network Performance. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13715. Springer, Cham. https://doi.org/10.1007/978-3-031-26409-2_38

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  • DOI: https://doi.org/10.1007/978-3-031-26409-2_38

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  • Online ISBN: 978-3-031-26409-2

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