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Multitask Hopfield Networks

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11907))

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Abstract

Multitask algorithms typically use task similarity information as a bias to speed up and improve the performance of learning processes. Tasks are learned jointly, sharing information across them, in order to construct models more accurate than those learned separately over single tasks. In this contribution, we present the first multitask model, to our knowledge, based on Hopfield Networks (HNs), named HoMTask. We show that by appropriately building a unique HN embedding all tasks, a more robust and effective classification model can be learned. HoMTask is a transductive semi-supervised parametric HN, that minimizes an energy function extended to all nodes and to all tasks under study. We provide theoretical evidence that the optimal parameters automatically estimated by HoMTask make coherent the model itself with the prior knowledge (connection weights and node labels). The convergence properties of HNs are preserved, and the fixed point reached by the network dynamics gives rise to the prediction of unlabeled nodes. The proposed model improves the classification abilities of singletask HNs on a preliminary benchmark comparison, and achieves competitive performance with state-of-the-art semi-supervised graph-based algorithms.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy

    Marco Frasca, Giuliano Grossi & Giorgio Valentini

Authors
  1. Marco Frasca
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  2. Giuliano Grossi
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  3. Giorgio Valentini
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Corresponding author

Correspondence to Marco Frasca .

Editor information

Editors and Affiliations

  1. Leuphana University, Lüneburg, Germany

    Ulf Brefeld

  2. IRISA/Inria, Rennes, France

    Elisa Fromont

  3. University of Würzburg, Würzburg, Germany

    Andreas Hotho

  4. Leiden University, Leiden, The Netherlands

    Arno Knobbe

  5. ETH Zurich, Zurich, Switzerland

    Marloes Maathuis

  6. Institut National des Sciences Appliquées, Villeurbanne, France

    Céline Robardet

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Frasca, M., Grossi, G., Valentini, G. (2020). Multitask Hopfield Networks. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11907. Springer, Cham. https://doi.org/10.1007/978-3-030-46147-8_21

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  • DOI: https://doi.org/10.1007/978-3-030-46147-8_21

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