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Evaluation of Oversampling Data Balancing Techniques in the Context of Ordinal Classification.

, , , , and . IJCNN, page 1-8. IEEE, (2018)

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Missing Image Data Imputation using Variational Autoencoders with Weighted Loss., , , , and . ESANN, page 475-480. (2020)Interpreting deep learning models for ordinal problems., , , and . ESANN, (2018)Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing., , , , , , , , , and 9 other author(s). iMIMIC/MIL3ID/LABELS@MICCAI, volume 12446 of Lecture Notes in Computer Science, page 1. Springer, (2020)An iterative oversampling approach for ordinal classification., , , , , and . SAC, page 771-774. ACM, (2019)Evaluating Post-hoc Interpretability with Intrinsic Interpretability., , , and . CoRR, (2023)Interpretability vs. Complexity: The Friction in Deep Neural Networks., , , and . IJCNN, page 1-7. IEEE, (2020)A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences., , , , , , , , , and 6 other author(s). Artif. Intell. Rev., 56 (4): 3473-3504 (April 2023)Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations., , , , and . Inf. Process. Manag., 60 (2): 103225 (2023)Evaluation of Oversampling Data Balancing Techniques in the Context of Ordinal Classification., , , , and . IJCNN, page 1-8. IEEE, (2018)