An Explicitly Relational Neural Network Architecture
Abstract
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data. In order to evaluate and analyse the architecture, we introduce a family of simple visual relational reasoning tasks of varying complexity. We show that the proposed architecture, when pre-trained on a curriculum of such tasks, learns to generate reusable representations that better facilitate subsequent learning on previously unseen tasks when compared to a number of baseline architectures. The workings of a successfully trained model are visualised to shed some light on how the architecture functions.
Cite
Text
Shanahan et al. "An Explicitly Relational Neural Network Architecture." International Conference on Machine Learning, 2020.Markdown
[Shanahan et al. "An Explicitly Relational Neural Network Architecture." International Conference on Machine Learning, 2020.](https://mlanthology.org/icml/2020/shanahan2020icml-explicitly/)BibTeX
@inproceedings{shanahan2020icml-explicitly,
title = {{An Explicitly Relational Neural Network Architecture}},
author = {Shanahan, Murray and Nikiforou, Kyriacos and Creswell, Antonia and Kaplanis, Christos and Barrett, David and Garnelo, Marta},
booktitle = {International Conference on Machine Learning},
year = {2020},
pages = {8593-8603},
volume = {119},
url = {https://mlanthology.org/icml/2020/shanahan2020icml-explicitly/}
}