4 citations · 4 across the 4 of their papers we have counts for
4 papers
Redundancy in Deep Linear Neural Networks
Oriel BenShmuel
Conventional wisdom states that deep linear neural networks benefit from expressiveness and optimization advantages over a single linear layer. This paper suggests that, in practic…
Early Transferability of Adversarial Examples in Deep Neural Networks
Oriel BenShmuel
This paper will describe and analyze a new phenomenon that was not known before, which we call "Early Transferability". Its essence is that the adversarial perturbations transfer a…
Meet You Halfway: Explaining Deep Learning Mysteries
Oriel BenShmuel
Deep neural networks perform exceptionally well on various learning tasks with state-of-the-art results. While these models are highly expressive and achieve impressively accurate…
The Dimpled Manifold Model of Adversarial Examples in Machine Learning
Adi Shamir, Odelia Melamed, Oriel BenShmuel
The extreme fragility of deep neural networks, when presented with tiny perturbations in their inputs, was independently discovered by several research groups in 2013. However, des…