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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

  • npm
    npm install npm@latest -g

Installation

  1. Get a free API Key at https://example.com
  2. Clone the repo
    git clone https://github.com/neu-spiral/LearnSetsImplicit.git
  3. Install NPM packages
    npm install
  4. Enter your API in config.js
    const API_KEY = 'ENTER YOUR API';
  5. Change git remote url to avoid accidental pushes to base project
    git remote set-url origin neu-spiral/LearnSetsImplicit
    git remote -v # confirm the changes
  6. Running Experiments

This project includes scripts to run both the baseline models and the proposed method.

Baseline Models

To run the baseline experiments, execute:

bash run_baseline.sh

This script launches training and evaluation for all baseline methods. Make sure any dataset paths and hyperparameters are correctly configured inside the script.


Proposed Method

To run our proposed method, use:

bash run_implicit.sh

This will launch the training pipeline for the proposed model as described in the paper.


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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

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Top contributors:

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License

Distributed under MIT License. See LICENSE for more information.

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Acknowledgments

  • We gratefully acknowledge support from the National Science Foundation (NSF-grant 1750539).

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Learning Set Functions with Neural Networks

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