# The Loss Surface of Deep and Wide Neural Networks

@article{Nguyen2017TheLS, title={The Loss Surface of Deep and Wide Neural Networks}, author={Quynh N. Nguyen and Matthias Hein}, journal={ArXiv}, year={2017}, volume={abs/1704.08045} }

While the optimization problem behind deep neural networks is highly non-convex, it is frequently observed in practice that training deep networks seems possible without getting stuck in suboptimal points. It has been argued that this is the case as all local minima are close to being globally optimal. We show that this is (almost) true, in fact almost all local minima are globally optimal, for a fully connected network with squared loss and analytic activation function given that the number of…

## 257 Citations

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