November 8, 2023 Bachelor Thesis Open Access

Oßwald, Paul

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    "creators": [
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        "affiliation": "KIT/ETP", 
        "name": "O\u00dfwald, Paul"
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    "description": "<p>In the last years, efforts have been made to use various machine learning models in the multiclass classification of the tt+X&nbsp;events. Especially Graph Neural Networks (GNN) architectures have received attention, due to the data structure lending itself to the representation as a multi-relational graph. Naturally, when using a GNN to classify an event, it is of interest why it makes a particular prediction. GNNs however, due to the complex structure of graph data and black-box nature, are hard to interpret.</p>\n\n<p>This thesis focuses on examining a GNN trained for this classification task using the GNNExplainer method to better understand the underlying decisions taken by the GNN when using different tt+X&nbsp;events as input.</p>", 
    "keywords": [
      "Graph Neural Networks", 
      "GNNExplainer", 
      "CMS", 
      "ttX", 
      "Machine learning"
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    "language": "eng", 
    "publication_date": "2023-11-08", 
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      "subtype": "bachelor-thesis", 
      "title": "Bachelor Thesis", 
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    "thesis": {
      "report_number": "ETP-BACHELOR-KA/2023-13", 
      "supervisors": [
        {
          "affiliation": "KIT/ETP", 
          "name": "Husemann, Prof. Dr. Ulrich"
        }, 
        {
          "affiliation": "KIT/ETP", 
          "name": "Wassmer, Dr. Michael"
        }, 
        {
          "affiliation": "KIT/ETP", 
          "name": "Pfeffer, Emanuel"
        }
      ]
    }, 
    "title": "Generating Explanations for Graph Neural Networks for tt+X events at the CMS Experiment"
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