ST-MoE: Spatio-Temporal Mixture-of-Experts for Debiasing in Traffic Prediction.
Shuhao Li|Yue Cui|Yan Zhao|Weidong Yang|Ruiyuan Zhang|Xiaofang Zhou
| Anthology ID: | DBLP:conf/cikm/LiCZYZ023 |
|---|---|
| Volume: | Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023 |
| Year: | 2023 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 1208-1217 |
| URL: | https://doi.org/10.1145/3583780.3615068 |
| DOI: | https://doi.org/10.1145/3583780.3615068 |
| DBLP: | conf/cikm/LiCZYZ023 |
| BibTeX: |
@inproceedings{li-2023-stmoe,
author = {Shuhao Li and
Yue Cui and
Yan Zhao and
Weidong Yang and
Ruiyuan Zhang and
Xiaofang Zhou},
editor = {Ingo Frommholz and
Frank Hopfgartner and
Mark Lee and
Michael P. Oakes and
Mounia Lalmas-Roelleke and
Min Zhang and
Rodrygo L. T. Santos},
title = {{ST-MoE: Spatio-Temporal Mixture-of-Experts for Debiasing in Traffic Prediction}},
booktitle = {{Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023}},
pages = {1208--1217},
publisher = {ACM},
year = {2023},
url = {https://doi.org/10.1145/3583780.3615068},
doi = {https://doi.org/10.1145/3583780.3615068}
}