https://github.com/ssydasheng/neural-kernel-network

Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326
https://github.com/ssydasheng/neural-kernel-network

extrapolation gaussian-processes kernel-methods machine-learning

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Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326

• Host: GitHub
• URL: https://github.com/ssydasheng/neural-kernel-network
• Owner: ssydasheng
• License: mit
• Created: 2017-10-28T01:54:14.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2018-07-10T12:21:22.000Z (over 6 years ago)
• Last Synced: 2024-04-28T03:34:50.852Z (8 months ago)
• Topics: extrapolation, gaussian-processes, kernel-methods, machine-learning
• Language: Python
• Homepage:
• Size: 2.7 MB
• Stars: 68
• Watchers: 9
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Neural Kernel Network

This code is jointly contributed by [Shengyang Sun](https://github.com/ssydasheng), [Guodong Zhang](https://github.com/gd-zhang), [Chaoqi Wang](https://github.com/alecwangcq) and [Wenyuan Zeng](https://github.com/joy820)

## 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

* [Tensorflow](https:www.tensorflow.org)

* [GPflow-Slim](https://github.com/ssydasheng/GPflow-Slim)

## Experiments

Below we shows some examples to run the experiments.

We also provide experiment figures and logging files in [results](./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}

}

```