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Adapting the Size of Artificial Neural Networks Using Dynamic Auto-Sizing

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00363052" target="_blank" >RIV/68407700:21240/22:00363052 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/abstract/document/10000471" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10000471</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/CSIT56902.2022.10000471" target="_blank" >10.1109/CSIT56902.2022.10000471</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Adapting the Size of Artificial Neural Networks Using Dynamic Auto-Sizing

  • Original language description

    We introduce dynamic auto-sizing, a novel approach to training artificial neural networks which allows the models to automatically adapt their size to the problem domain. The size of the models can be further controlled during the learning process by modifying the applied strength of regularization. The ability of dynamic auto-sizing models to expand or shrink their hidden layers is achieved by periodically growing and pruning entire units such as neurons or filters. For this purpose, we introduce weighted L1 regularization, a novel regularization method for inducing structured sparsity. Besides analyzing the behavior of dynamic auto-sizing, we evaluate predictive performance of models trained using the method and show that such models can provide a predictive advantage over traditional approaches.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    IEEE 17th International Conference on Computer Science and Information Technologies

  • ISBN

    979-8-3503-3431-9

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    592-596

  • Publisher name

    IEEE

  • Place of publication

    Dortmund

  • Event location

    Lvov

  • Event date

    Nov 10, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000927642900139