1 citations · 1 across the 3 of their papers we have counts for
5 papers
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…
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…
Algebraic Adversarial Attacks on Integrated Gradients
Lachlan Simpson, Federico Costanza, Kyle Millar +3
Adversarial attacks on explainability models have drastic consequences when explanations are used to understand the reasoning of neural networks in safety critical systems. Path me…
Probabilistic Lipschitzness and the Stable Rank for Comparing Explanation Models
Lachlan Simpson, Kyle Millar, Adriel Cheng +2
Explainability models are now prevalent within machine learning to address the black-box nature of neural networks. The question now is which explainability model is most effective…
A Testbed for Automating and Analysing Mobile Devices and their Applications
Lachlan Simpson, Kyle Millar, Adriel Cheng +2
The need for improved network situational awareness has been highlighted by the growing complexity and severity of cyber-attacks. Mobile phones pose a significant risk to network s…