Deep Neural Networks vs. Gaussian Processes: Similarities, Differences, and Trade-Offs

One axis along which to quantify the differences between these two models is by considering the number and types of parameters in each framework. In general, since Gaussian Processes are considered non-parametric machine learning techniques, Gaussian Processes (GPs) learn significantly fewer parameters, and predictions are largely driven by the training dataset over which they are defined.