1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2025★ 1 cited
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…
cs.LG2024
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…
cs.NI2023
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…