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Increasing biases can be more efficient than increasing weights

Author

Listed:
  • Carlo Metta

    (ISTI-CNR)

  • Marco Fantozzi

    (University of Parma)

  • Andrea Papini

    (Scuola Normale Superiore)

  • Gianluca Amato

    (University of Chieti-Pescara)

  • Matteo Bergamaschi

    (University of Padova)

  • Andrea Fois

    (University of Parma)

  • Silvia Giulia Galfrè

    (University of Pisa)

  • Alessandro Marchetti

    (University of Chieti-Pescara)

  • Michelangelo Vegliò

    (University of Chieti-Pescara)

  • Maurizio Parton

    (University of Chieti-Pescara)

  • Francesco Morandin

    (University of Parma)

Abstract

We introduce a novel computational unit for neural networks that features multiple biases, challenging the traditional perceptron structure. This unit emphasizes the importance of preserving uncorrupted information as it is passed from one unit to the next, applying activation functions later in the process with specialized biases for each unit. Through both empirical and theoretical analyses, we show that by focusing on increasing biases rather than weights, there is potential for significant enhancement in a neural network model’s performance. This approach offers an alternative perspective on optimizing information flow within neural networks. See source code (CurioSAI in Increasing biases can be more efficient than increasing weights, 2023. https://github.com/CuriosAI/dac-dev ).

Suggested Citation

  • Carlo Metta & Marco Fantozzi & Andrea Papini & Gianluca Amato & Matteo Bergamaschi & Andrea Fois & Silvia Giulia Galfrè & Alessandro Marchetti & Michelangelo Vegliò & Maurizio Parton & Francesco Moran, 2025. "Increasing biases can be more efficient than increasing weights," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 19(2), pages 437-468, June.
    • Handle: RePEc:spr:advdac:v:19:y:2025:i:2:d:10.1007_s11634-025-00649-2
    DOI: 10.1007/s11634-025-00649-2
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