The Outer Product Structure of Neural Network Derivatives

9 Oct 2018Craig BakkerMichael J. HenryNathan O. Hodas

In this paper, we show that feedforward and recurrent neural networks exhibit an outer product derivative structure but that convolutional neural networks do not. This structure makes it possible to use higher-order information without needing approximations or infeasibly large amounts of memory, and it may also provide insights into the geometry of neural network optima... (read more)

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