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Bridging Neural and Symbolic Representations with Transitional Dictionary Learning

Installation

  1. First clone the directory.
git submodule init; git submodule update

(If showing error of no permission, need to first add a new SSH key to your GitHub account.)

  1. Install dependencies.

Create a new environment using conda, with Python >= 3.10.6 Install PyTorch (version >= 2.0.0). The repo is tested with PyTorch version of 1.10.1 and there is no guarentee that other version works. Then install other dependencies via:

pip install -r requirements.txt

Dataset

The dataset files for LineWorld and LW-G can be generated using the BabyARC engine with the datasets/BabyARC submodule, the OmniGlot dataset found here, and ShapeNet can be downloaded here, the code for preprocessing the datasets are under dataset/ or directly downloaded via this link. If download from the above link, put the downloaded data under the ./datasets/files/ folder.

Download from this anonymous link: https://drive.google.com/file/d/15S1RsDy_5MdNq_iUsZCPsRHd9qMqdTBy/view?usp=share_link

Structure

Here we detail the repo's structure:

  • The datasets contains the codes for loading and processing the datasets.
  • The TAE.py is the implementation of model
  • The SDE.py is the implementation of the diffusion model
  • The attention.py is the implementation of the attention layers
  • The sampler.py is a larger diffusion model based on DDPM
  • The pltrain.py is a basic training script using pytorch lightning
  • All files wil 3D are the 3D version of the file for handling 3D voxels.

Citation

If you find our work and/or our code useful, please cite us via:

@inproceedings{
cheng2024bridging,
title={Bridging Neural and Symbolic Representations with Transitional Dictionary Learning},
author={Junyan Cheng and Peter Chin},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=uqxBTcWRnj}
}