5 citations · 5 across the 1 of their papers we have counts for
2 papers
cs.LG2020
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses
Charles G. Frye, James Simon, Neha S. Wadia +3
Despite the fact that the loss functions of deep neural networks are highly non-convex, gradient-based optimization algorithms converge to approximately the same performance from m…
cs.LG2019★ 5 cited
Numerically Recovering the Critical Points of a Deep Linear Autoencoder
Charles G. Frye, Neha S. Wadia, Michael R. DeWeese +1
Numerically locating the critical points of non-convex surfaces is a long-standing problem central to many fields. Recently, the loss surfaces of deep neural networks have been exp…