Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning.

Xuanhua Yang|Jianxin Zhao|Shaoguo Liu|Liang Wang|Bo Zheng


Anthology ID:DBLP:conf/sigir/YangZLWZ23
Volume:Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023
Year:2023
Venue:Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
Publisher:ACM
Pages:2032-2036
URL:https://doi.org/10.1145/3539618.3591993
DOI:https://doi.org/10.1145/3539618.3591993
DBLP:conf/sigir/YangZLWZ23
BibTeX:
@inproceedings{yang-2023-gradient, author = {Xuanhua Yang and Jianxin Zhao and Shaoguo Liu and Liang Wang and Bo Zheng}, editor = {Hsin-Hsi Chen and Wei-Jou (Edward) Duh and Hen-Hsen Huang and Makoto P. Kato and Josiane Mothe and Barbara Poblete}, title = {{Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning}}, booktitle = {{Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023}}, pages = {2032--2036}, publisher = {ACM}, year = {2023}, url = {https://doi.org/10.1145/3539618.3591993}, doi = {https://doi.org/10.1145/3539618.3591993} }