GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning.
Xin Dong|Ruize Wu|Chao Xiong|Hai Li|Lei Cheng|Yong He|Shiyou Qian|Jian Cao|Linjian Mo
| Anthology ID: | DBLP:conf/cikm/DongWXLCHQ0M22 |
|---|---|
| Volume: | Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022 |
| Year: | 2022 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 386-395 |
| URL: | https://doi.org/10.1145/3511808.3557333 |
| DOI: | https://doi.org/10.1145/3511808.3557333 |
| DBLP: | conf/cikm/DongWXLCHQ0M22 |
| BibTeX: |
@inproceedings{dong-2022-gdod,
author = {Xin Dong and
Ruize Wu and
Chao Xiong and
Hai Li and
Lei Cheng and
Yong He and
Shiyou Qian and
Jian Cao and
Linjian Mo},
editor = {Mohammad Al Hasan and
Li Xiong},
title = {{GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning}},
booktitle = {{Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022}},
pages = {386--395},
publisher = {ACM},
year = {2022},
url = {https://doi.org/10.1145/3511808.3557333},
doi = {https://doi.org/10.1145/3511808.3557333}
}