Thomas Tanay

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Deep neural networks have been shown to suffer from a surprising weakness: their classification outputs can be changed by small, non-random perturbations of their inputs. This adversarial example phenomenon has been explained as originating from deep networks being"too linear"(Goodfellow et al., 2014). We show here that the linear explanation of adversarial(More)
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