10 citations · 23 across the 4 of their papers we have counts for
Showing 2018Show all
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stat.ML2018★ 6 cited
Lipschitz Networks and Distributional Robustness
Zac Cranko, Simon Kornblith, Zhan Shi +1
Robust risk minimisation has several advantages: it has been studied with regards to improving the generalisation properties of models and robustness to adversarial perturbation. W…
cs.LG2018★ 10 cited
Monge blunts Bayes: Hardness Results for Adversarial Training
Zac Cranko, Aditya Krishna Menon, Richard Nock +3
The last few years have seen a staggering number of empirical studies of the robustness of neural networks in a model of adversarial perturbations of their inputs. Most rely on an…