10 citations · 14 across the 3 of their papers we have counts for
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cs.LG2023★ 10 cited
Are Defenses for Graph Neural Networks Robust?
Felix Mujkanovic, Simon Geisler, Stephan Günnemann +1
A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard met…
cs.LG2022★ 2 cited
On the Robustness and Anomaly Detection of Sparse Neural Networks
Morgane Ayle, Bertrand Charpentier, John Rachwan +3
The robustness and anomaly detection capability of neural networks are crucial topics for their safe adoption in the real-world. Moreover, the over-parameterization of recent netwo…