2018
Autores
Domingues, I; Amorim, JP; Abreu, PH; Duarte, H; Santos, JAM;
Publicação
2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018
Abstract
2018
Autores
Soares, JP; Santos, MS; Abreu, PH; Araújo, H; Santos, JAM;
Publicação
Advances in Intelligent Data Analysis XVII - 17th International Symposium, IDA 2018, 's-Hertogenbosch, The Netherlands, October 24-26, 2018, Proceedings
Abstract
2018
Autores
Costa, AF; Santos, MS; Soares, JP; Abreu, PH;
Publicação
Advances in Intelligent Data Analysis XVII - 17th International Symposium, IDA 2018, 's-Hertogenbosch, The Netherlands, October 24-26, 2018, Proceedings
Abstract
2018
Autores
Amorim, JP; Domingues, I; Abreu, PH; Santos, JAM;
Publicação
26th European Symposium on Artificial Neural Networks, ESANN 2018, Bruges, Belgium, April 25-27, 2018
Abstract
Machine learning algorithms have evolved by exchanging simplicity and interpretability for accuracy, which prevents their adoption in critical tasks such as healthcare. Progress can be made by improving interpretability of complex models while preserving performance. This work introduces an extension of interpretable mimic learning which teaches in-terpretable models to mimic predictions of complex deep neural networks, not only on binary problems but also in ordinal settings. The results show that the mimic models have comparative performance to Deep Neural Network models, with the advantage of being interpretable.
2018
Autores
Frazão, I; Abreu, PH; Cruz, T; Araújo, H; Simões, P;
Publicação
Critical Information Infrastructures Security - 13th International Conference, CRITIS 2018, Kaunas, Lithuania, September 24-26, 2018, Revised Selected Papers
Abstract
2019
Autores
Domingues, I; Sampaio, I; Duarte, H; Santos, JAM; Abreu, PH;
Publicação
IEEE Access
Abstract
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