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International Conference on Artificial Neural Networks

ICANN 2020: Artificial Neural Networks and Machine Learning – ICANN 2020 pp 180–191Cite as

Hierarchical Modeling with Neurodynamical Agglomerative Analysis

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12396)

Abstract

We propose a new analysis technique for neural networks, Neurodynamical Agglomerative Analysis (NAA), an analysis pipeline designed to compare class representations within a given neural network model. The proposed pipeline results in a hierarchy of class relationships implied by the network representation, i.e. a semantic hierarchy analogous to a human-made ontological view of the relevant classes. We use networks pretrained on the ImageNet benchmark dataset to infer semantic hierarchies and show the similarity to human-made semantic hierarchies by comparing them with the WordNet ontology. Further, we show using MNIST training experiments that class relationships extracted using NAA appear to be invariant to random weight initializations, tending toward equivalent class relationships across network initializations in sufficiently parameterized networks.

Keywords

  • Neural network theory
  • Deep learning
  • Cognitive models

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - GRK 2340.

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Author information

Authors and Affiliations

  1. Universität Osnabrück, Wachsbleiche 27, 49090, Osnabrück, Germany

    Michael Marino, Georg Schröter, Gunther Heidemann & Joachim Hertzberg

  2. DFKI Laboratory Niedersachsen, Plan-Based Robot Control Department, Berghoffstr. 11, 49090, Osnabrück, Germany

    Joachim Hertzberg

Authors
  1. Michael Marino
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  2. Georg Schröter
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  3. Gunther Heidemann
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  4. Joachim Hertzberg
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Corresponding author

Correspondence to Michael Marino .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Comenius University in Bratislava, Bratislava, Slovakia

    Prof. Igor Farkaš

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark

    Paolo Masulli

  3. Department of Informatics, University of Hamburg, Hamburg, Germany

    Stefan Wermter

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Marino, M., Schröter, G., Heidemann, G., Hertzberg, J. (2020). Hierarchical Modeling with Neurodynamical Agglomerative Analysis. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_15

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  • DOI: https://doi.org/10.1007/978-3-030-61609-0_15

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