2,925 research outputs found
Generalization in Deep Learning
This paper provides theoretical insights into why and how deep learning can
generalize well, despite its large capacity, complexity, possible algorithmic
instability, nonrobustness, and sharp minima, responding to an open question in
the literature. We also discuss approaches to provide non-vacuous
generalization guarantees for deep learning. Based on theoretical observations,
we propose new open problems and discuss the limitations of our results.Comment: To appear in Mathematics of Deep Learning, Cambridge University
Press. All previous results remain unchange
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