@article {48132,
	title = {An empirical analysis of deep network loss surfaces.},
	journal = {arXiv},
	year = {2016},
	month = {2016 Dec 13},
	pages = {arXiv:1612.04010},
	abstract = {<p>The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find minima.</p>
},
	url = {https://arxiv.org/pdf/1612.04010.pdf},
	author = {Im, Daniel Jiwoong and Tao, Michael and Branson, Kristin}
}
