And/or trade-off in artificial neurons: impact on adversarial robustness. (arXiv:2102.07389v2 [cs.LG] UPDATED)

Since its discovery in 2013, the phenomenon of adversarial examples has
attracted a growing amount of attention from the machine learning community. A
deeper understanding of the problem could lead to a better comprehension of how
information is processed and encoded in neural networks and, more in general,
could help to solve the issue of interpretability in machine learning. Our idea
to increase adversarial resilience starts with the observation that artificial
neurons can be divided in two broad categories: AND-like …

artificial impact lg trade