IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v627y2023ics0378437123006982.html
   My bibliography  Save this article

Dense Hebbian neural networks: A replica symmetric picture of unsupervised learning

Author

Listed:
  • Agliari, Elena
  • Albanese, Linda
  • Alemanno, Francesco
  • Alessandrelli, Andrea
  • Barra, Adriano
  • Giannotti, Fosca
  • Lotito, Daniele
  • Pedreschi, Dino

Abstract

We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via statistical-mechanics tools, and numerically, via Monte Carlo simulations. In particular, we obtain a phase diagram summarizing their performance as a function of the control parameters (e.g. quality and quantity of the training dataset, network storage, noise) that is valid in the limit of large network size and structureless datasets. Moreover, we establish a bridge between macroscopic observables standardly used in statistical mechanics and loss functions typically used in the machine learning.

Suggested Citation

  • Agliari, Elena & Albanese, Linda & Alemanno, Francesco & Alessandrelli, Andrea & Barra, Adriano & Giannotti, Fosca & Lotito, Daniele & Pedreschi, Dino, 2023. "Dense Hebbian neural networks: A replica symmetric picture of unsupervised learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 627(C).
    • Handle: RePEc:eee:phsmap:v:627:y:2023:i:c:s0378437123006982
    DOI: 10.1016/j.physa.2023.129143
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123006982
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2023.129143?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Centonze, Martino Salomone & Kanter, Ido & Barra, Adriano, 2024. "Statistical mechanics of learning via reverberation in bidirectional associative memories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:627:y:2023:i:c:s0378437123006982. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.