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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Author

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  • Decebal Constantin Mocanu

    (Eindhoven University of Technology
    Eindhoven University of Technology)

  • Elena Mocanu

    (Eindhoven University of Technology
    Eindhoven University of Technology)

  • Peter Stone

    (The University of Texas at Austin)

  • Phuong H. Nguyen

    (Eindhoven University of Technology)

  • Madeleine Gibescu

    (Eindhoven University of Technology)

  • Antonio Liotta

    (University of Derby)

Abstract

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős–Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.

Suggested Citation

  • Decebal Constantin Mocanu & Elena Mocanu & Peter Stone & Phuong H. Nguyen & Madeleine Gibescu & Antonio Liotta, 2018. "Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04316-3
    DOI: 10.1038/s41467-018-04316-3
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    Cited by:

    1. Tobias Thomas & Dominik Straub & Fabian Tatai & Megan Shene & Tümer Tosik & Kristian Kersting & Constantin A. Rothkopf, 2024. "Modelling dataset bias in machine-learned theories of economic decision-making," Nature Human Behaviour, Nature, vol. 8(4), pages 679-691, April.
    2. Hirsch, Christian & Neumann, Matthias & Schmidt, Volker, 2023. "Asymptotic properties of one-layer artificial neural networks with sparse connectivity," Statistics & Probability Letters, Elsevier, vol. 193(C).
    3. Neil Kichler & Sher Afghan & Uwe Naumann, 2023. "Towards Sobolev Pruning," Papers 2312.03510, arXiv.org, revised Dec 2023.

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