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2020 | OriginalPaper | Chapter

Hierarchical Modeling with Neurodynamical Agglomerative Analysis

Authors : Michael Marino, Georg Schröter, Gunther Heidemann, Joachim Hertzberg

Published in: Artificial Neural Networks and Machine Learning – ICANN 2020

Publisher: Springer International Publishing

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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.

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Metadata
Title
Hierarchical Modeling with Neurodynamical Agglomerative Analysis
Authors
Michael Marino
Georg Schröter
Gunther Heidemann
Joachim Hertzberg
Copyright Year
2020
Book
Artificial Neural Networks and Machine Learning – ICANN 2020

Print ISBN: 978-3-030-61608-3

Electronic ISBN: 978-3-030-61609-0

Copyright Year: 2020

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

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