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20162019
most citedAdversarial Deep Learning for Robust Detection of Binary Encoded Malware

38 citations · 181 across the 15 of their papers we have counts for

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cs.NE2018★ 12 cited

Lipizzaner: A System That Scales Robust Generative Adversarial Network Training

Tom Schmiedlechner, Ignavier Ng Zhi Yong, Abdullah Al-Dujaili +2

GANs are difficult to train due to convergence pathologies such as mode and discriminator collapse. We introduce Lipizzaner, an open source software system that allows machine lear…

cs.NE2018★ 25 cited

Towards Distributed Coevolutionary GANs

Abdullah Al-Dujaili, Tom Schmiedlechner, and Erik Hemberg +1

Generative Adversarial Networks (GANs) have become one of the dominant methods for deep generative modeling. Despite their demonstrated success on multiple vision tasks, GANs are d…