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Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
A Pareto GAN leverages extreme value theory and the functional properties of neural networks to learn a distribution that matches the asymptotic behavior of the marginal distributions of the features.
Limitations of the Lipschitz constant as a defense against adversarial examples
Several recent papers have discussed utilizing Lipschitz constants to limit the susceptibility of neural networks to adversarial examples.
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