no code implementations • 24 Oct 2023 • Minfang Lu, Yuchen Jiang, Huihui Dong, Qi Li, Ziru Xu, Yuanlin Liu, Lixia Wu, Haoyuan Hu, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng
The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations.
1 code implementation • 26 Oct 2022 • Tianyu Liu, Yuchen Jiang, Nicholas Monath, Ryan Cotterell, Mrinmaya Sachan
Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks.
Ranked #1 on Relation Extraction on CoNLL04 (RE+ Micro F1 metric)
no code implementations • 20 Jun 2022 • Yuchen Jiang, Qi Li, Han Zhu, Jinbei Yu, Jin Li, Ziru Xu, Huihui Dong, Bo Zheng
Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously.
no code implementations • 8 Dec 2021 • Zelin Ren, Xuebing Yang, Yuchen Jiang, Wensheng Zhang
In this work, to deal with the two drawbacks, a learnable faster realization of the conventional KPCA is proposed.
no code implementations • 10 Sep 2019 • Zhenxin Xiao, Puyudi Yang, Yuchen Jiang, Kai-Wei Chang, Cho-Jui Hsieh
Adversarial example generation becomes a viable method for evaluating the robustness of a machine learning model.