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Graph-based Integrated Gradients for Explaining Graph Neural Networks
Lachlan Simpson, Kyle Millar, Adriel Cheng +2
Integrated Gradients (IG) is a common explainability technique to address the black-box problem of neural networks. Integrated gradients assumes continuous data. Graphs are discret…
Algebraic Adversarial Attacks on Explainability Models
Lachlan Simpson, Federico Costanza, Kyle Millar +3
Classical adversarial attacks are phrased as a constrained optimisation problem. Despite the efficacy of a constrained optimisation approach to adversarial attacks, one cannot trac…
Tangentially Aligned Integrated Gradients for User-Friendly Explanations
Lachlan Simpson, Federico Costanza, Kyle Millar +3
Integrated gradients is prevalent within machine learning to address the black-box problem of neural networks. The explanations given by integrated gradients depend on a choice of…