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Meet You Halfway: Explaining Deep Learning Mysteries. (arXiv:2206.04463v1 [cs.LG])
June 10, 2022, 1:20 a.m. | Oriel BenShmuel
cs.CR updates on arXiv.org arxiv.org
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 solutions with excellent generalization
abilities, they are susceptible to minor perturbations. Samples that suffer
such perturbations are known as "adversarial examples". Even though deep
learning is an extensively researched field, many questions about the nature of
deep learning models remain unanswered. In this paper, we introduce a new
conceptual framework attached with a formal description that aims …
More from arxiv.org / cs.CR updates on arXiv.org
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