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Quantum adversarial machine learning is an emerging field that studies the vulnerability of quantum learning systems against adversarial perturbations and develops possible defense strategies.Quantum universal adversarial perturbations are small perturbations, which can make different input samples into adversarial examples Baby Wash that may decei

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit.Specifically, we do so by calculating the exponential probabi

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