José Pereira Amorim

According to our database, José Pereira Amorim authored at least 9 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, Wessel Reijers, Adrien Depeursinge, Vincent Andrearczyk, Henning Müller

Artif. Intell. Rev., April, 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

Evaluating Post-hoc Interpretability with Intrinsic Interpretability.

José Pereira Amorim, Pedro Henriques Abreu, João A. M. Santos, Henning Müller

CoRR, 2023

2020

Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing.

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

Proceedings of the Interpretable and Annotation-Efficient Learning for Medical Image Computing, 2020

Interpretability vs. Complexity: The Friction in Deep Neural Networks.

José Pereira Amorim, Pedro H. Abreu, Mauricio Reyes, João A. M. Santos

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

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

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2019

An iterative oversampling approach for ordinal classification.

Francisco Marques, Hugo Duarte, João A. M. Santos, Inês Domingues, José Pereira Amorim, Pedro H. Abreu

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019

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

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Interpreting deep learning models for ordinal problems.

José Pereira Amorim, Inês Domingues, Pedro Henriques Abreu, João A. M. Santos

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018