José Pereira Amorim

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 9

Top Topics

Learning Models

Data Imputation

Ordinal Classification

Social Sciences

Top Venues

Artif. Intell. Rev.

Publications

José Pereira Amorim, Pedro Henriques Abreu, João A. M. Santos, Henning Müller
Evaluating Post-hoc Interpretability with Intrinsic Interpretability. CoRR (2023)
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) (2023)
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) (2023)
Jaime S. Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo 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)
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)
José Pereira Amorim, Pedro H. Abreu, Mauricio Reyes, João A. M. Santos
Interpretability vs. Complexity: The Friction in Deep Neural Networks. IJCNN (2020)
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)
José Pereira Amorim, Inês Domingues, Pedro Henriques Abreu, João A. M. Santos
Interpreting deep learning models for ordinal problems. ESANN (2018)
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)