Scalable Coupling of Deep Learning with Logical Reasoning
Abstract
In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimization problems from natural inputs. In this paper, we introduce a scalable neural architecture and loss function dedicated to learning the constraints and criteria of NP-hard reasoning problems expressed as discrete Graphical Models. We empirically show our loss function is able to efficiently learn how to solve NP-hard reasoning problems from natural inputs as the symbolic, visual or many-solutions Sudoku problems as well as the energy optimization formulation of the protein design problem, providing data efficiency, interpretability, and a posteriori control over predictions.
Cite
Text
Defresne et al. "Scalable Coupling of Deep Learning with Logical Reasoning." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/402Markdown
[Defresne et al. "Scalable Coupling of Deep Learning with Logical Reasoning." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/defresne2023ijcai-scalable/) doi:10.24963/IJCAI.2023/402BibTeX
@inproceedings{defresne2023ijcai-scalable,
title = {{Scalable Coupling of Deep Learning with Logical Reasoning}},
author = {Defresne, Marianne and Barbe, Sophie and Schiex, Thomas},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {3615-3623},
doi = {10.24963/IJCAI.2023/402},
url = {https://mlanthology.org/ijcai/2023/defresne2023ijcai-scalable/}
}