8 citations · 23 across the 18 of their papers we have counts for
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stat.ML2021★ 7 cited
Adversarial Attacks on Graph Classification via Bayesian Optimisation
Xingchen Wan, Henry Kenlay, Binxin Ru +3
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majorit…
stat.ML2021
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks
Arno Blaas, Stephen J. Roberts
It is desirable, and often a necessity, for machine learning models to be robust against adversarial attacks. This is particularly true for Bayesian models, as they are well-suited…