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<!DOCTYPE html>
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  <meta name="description" content="Explainability of Point Cloud Neural Networks Using SMILE">
  <meta name="keywords" content="XAI, Explainability, SMILE, Point Cloud, SafeML">
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  <title>Explainability with SMILE</title>


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        <h1 class="title">Explainability of Point Cloud Neural Networks Using SMILE</h1>
        <p class="subtitle">Statistical Model-Agnostic Interpretability with Local Explanations</p>
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      <h2 class="title is-3">Abstract</h2>
      <p>This study explores the implementation of SMILE for Point Cloud offering enhanced robustness and
        interpretability, particularly when Anderson-Darling distance is used. The approach demonstrates superior
        performance in terms of fidelity loss, R<sup>2</sup> scores, and robustness across various kernel widths,
        perturbation numbers, and clustering configurations.</p>
      <p>Additionally, a stability analysis using the Jaccard index establishes a benchmark for model stability in point
        cloud classification, identifying dataset biases crucial for safety-critical applications like autonomous
        driving.</p>
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      <h2 class="title">BibTeX</h2>
      <pre><code>
      @article{aslansefat2024pointcloud,
        title={Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local Explanations},
        author={Aslansefat, Koorosh and others},
        journal={IEEE Software},
        year={2024},
        publisher={IEEE}
      }
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