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A supplementary code for Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs.
Jupyter Notebook C Python C++
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README.md

PRODIGE: Probabilistic Differentiable Graph Embeddings

A supplementary code for Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs. https://arxiv.org/pdf/1910.03524.pdf

What does it do?

It learns weighted graph representation for your data end-to-end by backpropagation.

What do i need to run it?

  • Get as many CPUs as you can
    • We do not support GPU pathfinding (yet)
  • Use any popular 64-bit Linux operating system
    • Tested on Ubuntu16.04, should work fine on most linux x64 and even MacOS;
    • On other operating systems we recommend using Docker, e.g. pytorch-docker
  • Install the libraries required to compile C++ parts of PRODIGE
    • sudo apt-get install gcc g++ libstdc++6 wget curl unzip git
    • sudo apt-get install swig3.0 (or just swig)

How do I run it?

  1. Clone or download this repo. cd yourself to it's root directory.
  2. Grab or build a working python enviromnent. Anaconda works fine.
  3. Install packages from requirements.txt
  • It is critical that you use torch >= 1.1, not 1.0 or earlier
  • You will also need jupyter or some other way to work with .ipynb files
  1. Open jupyter notebook in ./notebooks/ and you're done!
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