Deep convolutional Gaussian processes

We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. e model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classication. We demonstrate greatly improved image classication performance compared to current Gaussian process approaches on the MNIST and CIFAR-10 datasets. In particular, we improve CIFAR-10 accuracy by over 10 percentage points.