no code implementations • ECNLP (ACL) 2022 • Bo Dong, Yiyi Wang, Hanbo Sun, Yunji Wang, Alireza Hashemi, Zheng Du
In this paper, we propose a contrastive meta-learning framework (CML) to address the challenges introduced by noisy annotated data, specifically in the context of natural language processing.
no code implementations • 24 May 2024 • Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu
Large language models have consistently struggled with complex reasoning tasks, such as mathematical problem-solving.
no code implementations • 18 Jan 2015 • Jim Jing-Yan Wang, Yunji Wang, Bing-Yi Jing, Xin Gao
To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework.