Interpreting deep learning models for ordinal problems. - José Pereira Amorim; Inês Domingues; Pedro Henriques Abreu; João A. M. Santos (ESANN, 2018)

Evaluation of Oversampling Data Balancing Techniques in the Context of Ordinal Classification. - Inês Domingues; José Pereira Amorim; Pedro H. Abreu; Hugo Duarte; João A. M. Santos (IJCNN, 2018)

An iterative oversampling approach for ordinal classification. - Francisco Marques; Hugo Duarte; João A. M. Santos; Inês Domingues; José Pereira Amorim; Pedro H. Abreu (SAC, 2019)

Missing Image Data Imputation using Variational Autoencoders with Weighted Loss. - Ricardo Cardoso Pereira; Joana Cristo Santos; José Pereira Amorim; Pedro Pereira Rodrigues; Pedro Henriques Abreu (ESANN, 2020)

Interpretability vs. Complexity: The Friction in Deep Neural Networks. - José Pereira Amorim; Pedro H. Abreu; Mauricio Reyes 001; João A. M. Santos (IJCNN, 2020)

Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing. - Jaime S. Cardoso 001; Hien Van Nguyen; Nicholas Heller; Pedro Henriques Abreu; Ivana Isgum; Wilson Silva; Ricardo P. M. Cruz; José Pereira Amorim; Vishal Patel 001; Badri Roysam; S. Kevin Zhou; Steve B. Jiang; et al. (iMIMIC/MIL3ID/LABELS@MICCAI, 2020)

Evaluating Post-hoc Interpretability with Intrinsic Interpretability. - José Pereira Amorim; Pedro Henriques Abreu; João A. M. Santos; Henning Müller (CoRR, 2023)

Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations. - José Pereira Amorim; Pedro H. Abreu; João A. M. Santos; Marc Cortes; Victor Vila (Inf. Process. Manag., 2023)

A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences. - Mara Graziani; Lidia Dutkiewicz; Davide Calvaresi; José Pereira Amorim; Katerina Yordanova; Mor Vered; Rahul Nair; Pedro Henriques Abreu; Tobias Blanke; Valeria Pulignano; John O. Prior; Lode Lauwaert; et al. (Artif. Intell. Rev., 2023)