On Adaptive Knowledge Distillation with Generalized KL-Divergence Loss for Ranking Model Refinement.
Yingrui Yang|Shanxiu He|Tao Yang
| Anthology ID: | DBLP:conf/ictir/YangHY24 |
|---|---|
| Volume: | Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2024, Washington, DC, USA, 13 July 2024 |
| Year: | 2024 |
| Venue: | International Conference on the Theory of Information Retrieval (ICTIR) |
| Publisher: | ACM |
| Pages: | 81-90 |
| URL: | https://doi.org/10.1145/3664190.3672522 |
| DOI: | https://doi.org/10.1145/3664190.3672522 |
| DBLP: | conf/ictir/YangHY24 |
| BibTeX: |
@inproceedings{yang-2024-adaptive,
author = {Yingrui Yang and
Shanxiu He and
Tao Yang},
editor = {Harrie Oosterhuis and
Hannah Bast and
Chenyan Xiong},
title = {{On Adaptive Knowledge Distillation with Generalized KL-Divergence Loss for Ranking Model Refinement}},
booktitle = {{Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2024, Washington, DC, USA, 13 July 2024}},
pages = {81--90},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3664190.3672522},
doi = {https://doi.org/10.1145/3664190.3672522}
}