Neural Logic Inductive Learning

Related tags

Deep LearningNLIL
Overview

Neural Logic Inductive Learning

This is the implementation of the Neural Logic Inductive Learning model (NLIL) proposed in the ICLR 2020 paper: Learn to Explain Efficiently via Neural Logic Inductive Learning. The Transformer implementation is based on this repo.

Requirements

  • python 3.6+
  • pytorch 1.1.0+
  • numpy
  • tqdm

Knowledge completion on WN18 and FB15K

You can run knowledge completion task on WN18 and FB15K with provided scripts

bash run_wn.sh
bash run_fb.sh

Object classification on Visual Genome

First, download the scene-graph dataset from the official site (click "Download Scene Graphs")

https://cs.stanford.edu/people/dorarad/gqa/download.html

Extract the files, and run the following script to generate the dataset

bash preprocess.sh path/to/the/sgraph/folder

Now you can run object classification with

bash run_gqa.sh

Reference

@inproceedings{
    yang2020learn,
    title={Learn to Explain Efficiently via Neural Logic Inductive Learning},
    author={Yuan Yang and Le Song},
    booktitle={International Conference on Learning Representations},
    year={2020},
    url={https://openreview.net/forum?id=SJlh8CEYDB}
}
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