R2V-MIF: Rule-to-Vector Contrastive Learning and Multi-Channel Information Fusion for Therapy Recommendation
Abstract
We propose a version of DFS designed for Constraint Programming, called bimodal DFS, that scales to both sparse and dense graphs. It runs in O(n + ~m) time, where ~m is the sum, for each vertex v, of the minimum between the numbers of successors and non-successors of v. Integrating it into Régin’s GAC algorithm for the AllDifferent constraint results in faster performance as the problem size increases, outperforming a GPU-accelerated version. In the vast majority of our tests, GAC now performs similarly to BC in terms of speed, but is able to solve more problems.
Cite
Text
Zhu et al. "R2V-MIF: Rule-to-Vector Contrastive Learning and Multi-Channel Information Fusion for Therapy Recommendation." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/291Markdown
[Zhu et al. "R2V-MIF: Rule-to-Vector Contrastive Learning and Multi-Channel Information Fusion for Therapy Recommendation." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/zhu2024ijcai-r/) doi:10.24963/ijcai.2024/291BibTeX
@inproceedings{zhu2024ijcai-r,
title = {{R2V-MIF: Rule-to-Vector Contrastive Learning and Multi-Channel Information Fusion for Therapy Recommendation}},
author = {Zhu, Nengjun and Huang, Jieyun and Cao, Jian and Hu, Liang and Yuan, Zixuan and Gao, Huanjing},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {2634-2641},
doi = {10.24963/ijcai.2024/291},
url = {https://mlanthology.org/ijcai/2024/zhu2024ijcai-r/}
}