On the Computational Efficiency of Training Neural Networks

Roi Livni; Shai Shalev-Shwartz; Ohad Shamir

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arXiv · 1410.1141

On the Computational Efficiency of Training Neural Networks

Roi Livni·Shai Shalev-Shwartz·Ohad Shamir

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Abstract

It is well-known that neural networks are computationally hard to train. On the other hand, in practice, modern day neural networks are trained efficiently using SGD and a variety of tricks that include different activation functions (e.g. ReLU), over-specification (i.e., train networks which are larger than needed), and regularization. In this paper we revisit the computational complexity of training neural networks from a modern perspective. We provide both positive and negative results, some of them yield new provably efficient and practical algorithms for training certain types of neural networks.

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Roi Livni, Shai Shalev-Shwartz, Ohad Shamir. 2014-10-28. On the Computational Efficiency of Training Neural Networks. https://arxiv.org/abs/1410.1141

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