Numerically Recovering the Critical Points of a Deep Linear Autoencoder
Charles G. Frye,
Neha S. Wadia,
Michael R. DeWeese
et al.
Abstract:Numerically locating the critical points of nonconvex surfaces is a long-standing problem central to many fields. Recently, the loss surfaces of deep neural networks have been explored to gain insight into outstanding questions in optimization, generalization, and network architecture design. However, the degree to which recentlyproposed methods for numerically recovering critical points actually do so has not been thoroughly evaluated. In this paper, we examine this issue in a case for which the ground truth … Show more
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