Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Shuming Ma"'
Autor:
Yuan Huang, Ruizhu Lin, Hongyu Li, Yujuan Xu, Fubao Tian, Liangchen Ma, Xiaoli Liu, Shuming Ma, Xiaolong Li, Zheying Lai, Chuanping Bai, Weichun He, Qi Ma, Jingkai Wang, Ning Zhu
Publikováno v:
Trials, Vol 24, Iss 1, Pp 1-11 (2023)
Abstract Background No consensus currently exists regarding the optimal protocol for repetitive transcranial magnetic stimulation (rTMS) treatment of upper-extremity motor dysfunction after stroke. Studies have shown that combined low- and high-frequ
Externí odkaz:
https://doaj.org/article/6437c319b3954eb99df534994ab1eb26
Autor:
Jian Yang, Yuwei Yin, Shuming Ma, Liqun Yang, Hongcheng Guo, Haoyang Huang, Dongdong Zhang, Yutao Zeng, Zhoujun Li, Furu Wei
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783031306747
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::286c0b820531b1966299249740f8e02b
https://doi.org/10.1007/978-3-031-30675-4_34
https://doi.org/10.1007/978-3-031-30675-4_34
Publikováno v:
Economic Analysis and Policy. 70:351-364
Using data for firms listed in China from 2007 to 2019, this paper explores the relationship between trade policy uncertainty (TPU) and firm risk taking (FRT). It finds that TPU is negatively and significantly correlated with FRT. Meanwhile, the infl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae0d7c2e7804a37b34b90103f4c3251e
https://doi.org/10.1016/j.eap.2021.03.007
https://doi.org/10.1016/j.eap.2021.03.007
Autor:
Jian Yang, Yuwei Yin, Liqun Yang, Shuming Ma, Haoyang Huang, Dongdong Zhang, Furu Wei, Zhoujun Li
Transformer structure, stacked by a sequence of encoder and decoder network layers, achieves significant development in neural machine translation. However, vanilla Transformer mainly exploits the top-layer representation, assuming the lower layers p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3715b6251d4eb5ed0aae9e26bf10f73
http://arxiv.org/abs/2207.14467
http://arxiv.org/abs/2207.14467
Publikováno v:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
Multilingual neural machine translation (MNMT) trained in multiple language pairs has attracted considerable attention due to fewer model parameters and lower training costs by sharing knowledge among multiple languages. Nonetheless, multilingual tra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2282de614a51dc5b69bb90c7cebea76
https://doi.org/10.24963/ijcai.2022/619
https://doi.org/10.24963/ijcai.2022/619
Autor:
Jian Yang, Yuwei Yin, Shuming Ma, Dongdong Zhang, Shuangzhi Wu, Hongcheng Guo, Zhoujun Li, Furu Wei
Publikováno v:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
Most translation tasks among languages belong to the zero-resource translation problem where parallel corpora are unavailable. Multilingual neural machine translation (MNMT) enables one-pass translation using shared semantic space for all languages c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2d7f9b108804f966d2f0c3589e87f75
https://doi.org/10.24963/ijcai.2022/618
https://doi.org/10.24963/ijcai.2022/618
Publikováno v:
ACS Omega, Vol 9, Iss 27, Pp 29544-29556 (2024)
Externí odkaz:
https://doaj.org/article/de48dd30c9034744b8565b321d7060ec
The Mixture-of-Experts (MoE) technique can scale up the model size of Transformers with an affordable computational overhead. We point out that existing learning-to-route MoE methods suffer from the routing fluctuation issue, i.e., the target expert
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e940afb43c8d209c15f04d6383ca3bd
http://arxiv.org/abs/2204.08396
http://arxiv.org/abs/2204.08396
Publikováno v:
AAAI
Zhoujun Li
Zhoujun Li
Language model pre-training has achieved success in many natural language processing tasks. Existing methods for cross-lingual pre-training adopt Translation Language Model to predict masked words with the concatenation of the source sentence and its
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3cbe638dc2ebf96b2a7045ba7178d08
https://doi.org/10.1609/aaai.v34i05.6480
https://doi.org/10.1609/aaai.v34i05.6480
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 32:374-387
We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61f7c08f614f7300de648c91fa473ace
https://doi.org/10.1109/tkde.2018.2883613
https://doi.org/10.1109/tkde.2018.2883613