• Deep belief nets can benefit a lot from unlabeled data when labeled data is scarce. – They just use the labeled data for fine-tuning. • Kernel methods, like Gaussian processes, work well on small labeled training sets but are slow for large training sets. • So when there is a lot of unlabeled data and only a little labeled data, combine the two approaches: – First learn a deep belief net without using the labels. – Then apply Gaussian process models to the deepest layer of features. This works better than using the raw data. – Then use GP’s to get the derivatives that are back- propagated through the deep belief net. This is a further win. It allows GP’s to fine-tune complicated domain-specific kernels.