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  • Semantic Artificial Neural Networks

    Author(s)
    Batsakis, S
    Tachmazidis, I
    Baryannis, G
    Antoniou, G
    Griffith University Author(s)
    Antoniou, Grigorios
    Year published
    2020
    Metadata
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    Abstract
    Neural networks have achieved in recent years human level performance in various application domains, including critical applications where accountability is a very important issue, closely related to the interpretability of neural networks and artificial intelligence in general. In this work, an approach for defining the structure of neural networks based on the conceptualisation and semantics of the application domain is proposed. The proposed approach, called Semantic Artificial Neural Networks, allows dealing with the problem of interpretability and also the definition of the structure of neural networks. In addition, ...
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    Neural networks have achieved in recent years human level performance in various application domains, including critical applications where accountability is a very important issue, closely related to the interpretability of neural networks and artificial intelligence in general. In this work, an approach for defining the structure of neural networks based on the conceptualisation and semantics of the application domain is proposed. The proposed approach, called Semantic Artificial Neural Networks, allows dealing with the problem of interpretability and also the definition of the structure of neural networks. In addition, the resulting neural networks are sparser and have fewer parameters than typical neural networks, while achieving high performance.
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    Conference Title
    Lecture Notes in Computer Science
    Volume
    12124
    DOI
    https://doi.org/10.1007/978-3-030-62327-2_7
    Subject
    Deep learning
    Neural networks
    Publication URI
    http://hdl.handle.net/10072/402041
    Collection
    • Conference outputs

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