ssydasheng / Neural Kernel Network
Licence: mit
Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326
Stars: ✭ 67
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Neural Kernel Network
This code is jointly contributed by Shengyang Sun, Guodong Zhang, Chaoqi Wang and Wenyuan Zeng
Introduction
Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" (https://arxiv.org/abs/1806.04326)
Dependencies
This project runs with Python 3.6. Before running the code, you have to install
Experiments
Below we shows some examples to run the experiments. We also provide experiment figures and logging files in results folder, as a reference.
Time Series
python exp/time-series.py --name airline --kern nkn
Regression
python exp/regression.py --data energy --split uci_woval --kern nkn
python exp/regression.py --data energy --split uci_woval_pca --kern nkn
Bayesian Optimization
python exp/bayes-opt.py --name sty --kern nkn --run 0
Texture Extrapolation
python exp/texture.py --data pave --kern nkn
Citation
To cite this work, please use
@article{sun2018differentiable,
title={Differentiable Compositional Kernel Learning for Gaussian Processes},
author={Sun, Shengyang and Zhang, Guodong and Wang, Chaoqi and Zeng, Wenyuan and Li, Jiaman and Grosse, Roger},
journal={arXiv preprint arXiv:1806.04326},
year={2018}
}
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