Deep Gaussian Processes with Doubly Stochastic Variational Inference
Requirements: GPflow 1.2.0
This code accompanies the paper
@inproceedings{salimbeni2017doubly, title={Doubly stochastic variational inference for deep gaussian processes}, author={Salimbeni, Hugh and Deisenroth, Marc}, booktitle={Advances in Neural Information Processing Systems}, year={2017} }
See the arxiv version at https://arxiv.org/abs/1705.08933
This code now offers additional functionality than in the above paper. In particular, natural gradients are now supported. If you use these, please consider citing the following paper:
@inproceedings{salimbeni2018natural, title={Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models}, author={Salimbeni, Hugh and Eleftheriadis, Stefanos and Hensman, James}, booktitle={Artificial Intelligence and Statistics}, year={2018} }