Web: http://arxiv.org/abs/2202.03932

Sept. 22, 2022, 1:12 a.m. | Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen, Alois Knoll

cs.LG updates on arXiv.org arxiv.org

As an emerging type of Neural Networks (NNs), Attention Networks (ATNs) such
as Transformers have been shown effective, in terms of accuracy, in many
applications. This paper further considers their robustness. More specifically,
we are curious about their maximum resilience against local input perturbations
compared to the more conventional Multi-Layer Perceptrons (MLPs). Thus, we
formulate the verification task into an optimization problem, from which exact
robustness values can be obtained. One major challenge, however, is the
non-convexity and non-linearity of …

arxiv attention networks robustness verification

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