# Deep Convolutional Networks as shallow Gaussian Processes

@article{GarrigaAlonso2018DeepCN, title={Deep Convolutional Networks as shallow Gaussian Processes}, author={Adri{\`a} Garriga-Alonso and Laurence Aitchison and Carl Edward Rasmussen}, journal={ArXiv}, year={2018}, volume={abs/1808.05587} }

We show that the output of a (residual) convolutional neural network (CNN) with an appropriate prior over the weights and biases is a Gaussian process (GP) in the limit of infinitely many convolutional filters, extending similar results for dense networks. For a CNN, the equivalent kernel can be computed exactly and, unlike "deep kernels", has very few parameters: only the hyperparameters of the original CNN. Further, we show that this kernel has two properties that allow it to be computed…

## 192 Citations

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