https://github.com/google-deepmind/mc_gradients

https://github.com/google-deepmind/mc_gradients

Last synced: 27 days ago
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• Host: GitHub
• URL: https://github.com/google-deepmind/mc_gradients
• Owner: google-deepmind
• License: apache-2.0
• Created: 2019-07-22T14:04:32.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T21:55:04.000Z (12 months ago)
• Last Synced: 2024-04-16T04:53:39.978Z (2 months ago)
• Language: Jupyter Notebook
• Size: 296 KB
• Stars: 151
• Watchers: 10
• Forks: 34
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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• awesome-stars - google-deepmind/mc_gradients - (Jupyter Notebook)

README

        
# Monte Carlo Gradient Estimation in Machine Learning

This is the example code for the following  paper.  If you use the code

here please cite this paper.

> Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih

  *Monte Carlo Gradient Estimation in Machine Learning*.  [\[arXiv\]](https://arxiv.org/abs/1906.10652).

## Running the code

The code contains:

  * the implementation the score function, pathwise and measure valued estimators `gradient_estimators.py` and their tests to ensure unbiasedness `gradient_estimators_test.py`.

  * the implementation of control variates `control_variates.py` and their tests `control_variates_tests.py`.

  * a `main.py` file to reproduce the Bayesian Logistic regression experiments in the paper.

  * a `config.py` file used to configure experiments.

To run the code and install the required dependencies:

```

  source monte_carlo_gradients/run.sh

```

To run a test:

```

  python3 -m monte_carlo_gradients.gradient_estimators_test

```

## Colab

You can run the code in the browser using [Colab](https://colab.research.google.com). The experiments from Section 3 can be reproduced using the following link: [Intuitive Analysis of Gradient Estimators](https://colab.research.google.com/github/deepmind/mc_gradients/blob/master/monte_carlo_gradients/variance_numerical_integration.ipynb)

## Disclaimer

This is not an official Google product.