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A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences.

, , , , , , , , , , , , , , , и . Artif. Intell. Rev., 56 (4): 3473-3504 (апреля 2023)

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José António Pereira Barros

José Carlos Fernandes Pereira

Carlos José Passos Pereira

Humberto Jorge José

Nadine José Gonzalez

 

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