239 citations
- Universidade Federal do CearáBR12 papers
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- Universidade Federal da ParaíbaBR5 papers
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- Universidade Federal do Rio Grande do NorteBR4 papers
- Centro Brasileiro de Pesquisas FísicasBR3 papers
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- Centre de Recherche en Informatique, Signal et Automatique de LilleFR1 paper
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46 papers
Thin-shell wormholes in cosmic voids
Jonathan A. Rebouças, Edson Otoniel, Francisco S. N. Lobo
Cosmic voids are underdense regions that can provide an effective large-scale environment with a de Sitter-like gravitational behavior. Motivated by recent black-hole solutions emb…
Bound Propagation meets Constraint Simplification: Improving Logic-based XAI for Neural Networks
Ronaldo Gomes, Jairo Ribeiro, Luiz Queiroz +1
Logic-based methods for explaining neural network decisions offer formal guarantees of correctness and non-redundancy, but they often suffer from high computational costs, especial…
Generalizing Logic-based Explanations for Machine Learning Classifiers via Optimization
Francisco Mateus Rocha Filho, Ajalmar Rêgo da Rocha Neto, Thiago Alves Rocha
Machine learning models support decision-making, yet the reasons behind their predictions are opaque. Clear and reliable explanations help users make informed decisions and avoid b…
Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy
Vinícius P. Chagas, Luiz H. T. Viana, Mac M. da S. Carlos +4
Sudden cardiac death (SCD) is unpredictable, and its prediction in Chagas cardiomyopathy (CC) remains a significant challenge, especially in patients not classified as high risk. W…
Enhancing Framingham Cardiovascular Risk Score Transparency through Logic-Based XAI
Emannuel L. de A. Bezerra, Luiz H. T. Viana, Vinícius P. Chagas +3
Cardiovascular disease (CVD) remains one of the leading global health challenges, accounting for more than 19 million deaths worldwide. To address this, several tools that aim to p…
Slice and Explain: Logic-Based Explanations for Neural Networks through Domain Slicing
Luiz Fernando Paulino Queiroz, Carlos Henrique Leitão Cavalcante, Thiago Alves Rocha
Neural networks (NNs) are pervasive across various domains but often lack interpretability. To address the growing need for explanations, logic-based approaches have been proposed…