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ORCIDJosé Pereira Amorim
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2020 – today
- 2023
[j2]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, Wessel Reijers, Adrien Depeursinge, Vincent Andrearczyk, Henning Müller:
A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences. Artif. Intell. Rev. 56(4): 3473-3504 (2023)- [j1]José Pereira Amorim, Pedro H. Abreu, João A. M. Santos, Marc Cortes, Victor Vila:
Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations. Inf. Process. Manag. 60(2): 103225 (2023) - [i1]José Pereira Amorim, Pedro Henriques Abreu, João A. M. Santos, Henning Müller:
Evaluating Post-hoc Interpretability with Intrinsic Interpretability. CoRR abs/2305.03002 (2023) - 2020
- [c6]Ricardo Cardoso Pereira, Joana Cristo Santos, José Pereira Amorim, Pedro Pereira Rodrigues, Pedro Henriques Abreu:
Missing Image Data Imputation using Variational Autoencoders with Weighted Loss. ESANN 2020: 475-480 - [c5]José Pereira Amorim, Pedro H. Abreu, Mauricio Reyes, João A. M. Santos:
Interpretability vs. Complexity: The Friction in Deep Neural Networks. IJCNN 2020: 1-7 - [c4]Jaime S. Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo P. M. Cruz, José Pereira Amorim, Vishal Patel, Badri Roysam, S. Kevin Zhou, Steve B. Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi-Sureshjani:
Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing. iMIMIC/MIL3ID/LABELS@MICCAI 2020: 1 - [e1]Jaime S. Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo P. M. Cruz, José Pereira Amorim, Vishal Patel, Badri Roysam, S. Kevin Zhou, Steve B. Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi-Sureshjani:
Interpretable and Annotation-Efficient Learning for Medical Image Computing - Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings. Lecture Notes in Computer Science 12446, Springer 2020, ISBN 978-3-030-61165-1 [contents]
2010 – 2019
- 2019
- [c3]Francisco Marques, Hugo Duarte, João A. M. Santos, Inês Domingues, José Pereira Amorim, Pedro H. Abreu:
An iterative oversampling approach for ordinal classification. SAC 2019: 771-774 - 2018
- [c2]José Pereira Amorim, Inês Domingues, Pedro Henriques Abreu, João A. M. Santos:
Interpreting deep learning models for ordinal problems. ESANN 2018 - [c1]Inês Domingues, José Pereira Amorim, Pedro H. Abreu, Hugo Duarte, João A. M. Santos:
Evaluation of Oversampling Data Balancing Techniques in the Context of Ordinal Classification. IJCNN 2018: 1-8
Coauthor Index
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