Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach. - Nicholas Halliwell; Freddy Lécué (CoRR, 2020)

User Scored Evaluation of Non-Unique Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs. - Nicholas Halliwell; Fabien Gandon; Freddy Lécué (K-CAP, 2021)

A Simplified Benchmark for Non-ambiguous Explanations of Knowledge Graph Link Prediction using Relational Graph Convolutional Networks. - Nicholas Halliwell; Fabien Gandon; Freddy Lécué (ISWC (Posters/Demos/Industry), 2021)

Linked Data Ground Truth for Quantitative and Qualitative Evaluation of Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs. - Nicholas Halliwell; Fabien Gandon; Freddy Lécué (WI/IAT, 2021)

Evaluating Explanations of Relational Graph Convolutional Network Link Predictions on Knowledge Graphs. - Nicholas Halliwell (AAAI, 2022)

A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction Using Relational Graph Convolutional Networks (Student Abstract). - Nicholas Halliwell; Fabien Gandon; Freddy Lécué (AAAI, 2022)

Impact of Injecting Ground Truth Explanations on Relational Graph Convolutional Networks and their Explanation Methods for Link Prediction on Knowledge Graphs. - Nicholas Halliwell; Fabien Gandon; Freddy Lécué (WI/IAT, 2022)

Evaluating and improving explanation quality of graph neural network link prediction on knowledge graphs. (Évaluation et amélioration de la qualité d'explication de la prédiction des liens par réseau neuronal sur les graphes de connaissances). - Nicholas Halliwell (2022)