Learning Non-overlapping Convolutional Neural Networks with Multiple Kernels

8 Nov 2017Kai ZhongZhao SongInderjit S. Dhillon

In this paper, we consider parameter recovery for non-overlapping convolutional neural networks (CNNs) with multiple kernels. We show that when the inputs follow Gaussian distribution and the sample size is sufficiently large, the squared loss of such CNNs is $\mathit{~locally~strongly~convex}$ in a basin of attraction near the global optima for most popular activation functions, like ReLU, Leaky ReLU, Squared ReLU, Sigmoid and Tanh... (read more)

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