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Theory of Deep Learning III: explaining the non-overfitting puzzle
(arXiv, 2017-12-30)
THIS MEMO IS REPLACED BY CBMM MEMO 90
A main puzzle of deep networks revolves around the absence of overfitting despite overparametrization and despite the large capacity demonstrated by zero training error on randomly ...
Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-11-23)
[formerly titled "Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review"]
The paper reviews and extends an emerging body of theoretical results on deep learning including the ...