Titlebook: Deep Learning Theory and Applications; Third International Ana Fred,Carlo Sansone,Kurosh Madani Conference proceedings 2023 The Editor(s)

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Emma Strebel,Ellen Kathrine Hansenional machine learning methods can be exploited. In particular, in the DeepWalk model, truncated random walks are employed in random walk-based approaches to capture structural links-connections between nodes. The SkipGram model is then applied to the truncated random walks to compute the embedded n

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https://doi.org/10.1007/978-3-642-39655-7nge of image domains. However, the model is only able to obtain such a high performance on in-distribution samples. On out-of-distribution samples, in contrast, the performance of the model may be significantly decreased. To detect out-of-distribution samples, Papernot and McDaniel [.] introduced a

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https://doi.org/10.1007/978-3-031-05014-5sing advances in computer vision and computational geometry. The conventional data processing workflow uses semantic segmentation to identify road points from three-dimensional (3D) automotive LiDAR point clouds, which have to be extended to determine its boundary points. The boundary points are cri