4 papers
From Abductive Explanations to Global Logical Rules for Node Classification in SGCs
Bryan Lima Cavalcante, Thiago Alves Rocha
Graph Neural Networks (GNNs) have achieved remarkable performance in node classification tasks, motivating growing interest in methods capable of explaining their predictions. Rece…
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…
Comparing Neural Network Encodings for Logic-based Explainability
Levi Cordeiro Carvalho, Saulo A. F. Oliveira, Thiago Alves Rocha
Providing explanations for the outputs of artificial neural networks (ANNs) is crucial in many contexts, such as critical systems, data protection laws and handling adversarial exa…
An Incremental MaxSAT-based Model to Learn Interpretable and Balanced Classification Rules
Antônio Carlos Souza Ferreira Júnior, Thiago Alves Rocha
The increasing advancements in the field of machine learning have led to the development of numerous applications that effectively address a wide range of problems with accurate pr…