Efficient Deep Gaussian Process Models for Variable-Sized Inputs - IJCNN 2019

[Paper]

Overview

Our proposed method combines Gaussian Processes with deep random feature expansion. This repository combines Gaussian processes (GP), deep random feature (DRF) model, and our GP-DRF model.

Requirements

• Pytorch version 0.4 or higher.

Running the methods

You can run each example as follows.

• For Gaussian processes,

python GP_example.py

• For the deep random feature expansion model,

python DRF_example.py

• For our GP-DRF model,

python GP_DRF_example.py

Citation

If you find this useful, please consider citing us!

@article{laradji2019efficient,
  title={Efficient Deep Gaussian Process Models for Variable-Sized Input},
  author={Laradji, Issam H and Schmidt, Mark and Pavlovic, Vladimir and Kim, Minyoung},
  journal={arXiv preprint arXiv:1905.06982},
  year={2019}
}