NIPS Proceedingsβ

On the Computational Efficiency of Training Neural Networks

Part of: Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Conference Event Type: Poster


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 neural networks.