We argue that a missing principle is making attention algorithmsIO-aware-- accounting for reads and writes between levels of GPU memory.

We argue that a missing principle is making attention algorithmsIO-aware-- accounting for reads and writes between levels of GPU memory.

Deep neural networks (DNNs) are well-known to be vulnerable to adversarial attacks, where malicious human-imperceptible perturbations are included inthe input to the deep network to fool it into making a wrong classification.

Inevitably these systems need to deal with the plethora of practical issues that arise from automation. One important aspect is being able to deal with corrupted or irregular data, either due to poor data collection, the presence of outliers, or adversarial attacks by malicious agents.

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