Zobrazeno 1 - 10
of 611
pro vyhledávání: '"Yu, Hai tao"'
Autor:
Wang, Yao, Liu, Xin, Kong, Weikun, Yu, Hai-Tao, Racharak, Teeradaj, Kim, Kyoung-Sook, Nguyen, Minh Le
Named Entity Recognition and Relation Extraction are two crucial and challenging subtasks in the field of Information Extraction. Despite the successes achieved by the traditional approaches, fundamental research questions remain open. First, most re
Externí odkaz:
http://arxiv.org/abs/2405.08311
Autor:
Yu, Hai-Tao, Song, Mofei
Publikováno v:
AAAI 2024
In perception, multiple sensory information is integrated to map visual information from 2D views onto 3D objects, which is beneficial for understanding in 3D environments. But in terms of a single 2D view rendered from different angles, only limited
Externí odkaz:
http://arxiv.org/abs/2402.10002
Publikováno v:
In Energy Conversion and Management 1 September 2024 315
Publikováno v:
In International Journal of Hospitality Management August 2024 121
Publikováno v:
In Journal of Alloys and Compounds 25 June 2024 989
Autor:
Yu, Hai-Tao, Gong, Jia-Yu, Xu, Wen-Hui, Chen, Yi-Ru, Li, Yue-Ting, Chen, Yi-Fei, Liu, Guo-Liang, Zhang, Hai-Ying, Xie, Lin
Publikováno v:
In The Journal of Nutrition February 2024 154(2):590-599
Autor:
Muramatsu, Naoya, Yu, Hai-Tao
With the continued innovations of deep neural networks, spiking neural networks (SNNs) that more closely resemble biological brain synapses have attracted attention owing to their low power consumption.However, for continuous data values, they must e
Externí odkaz:
http://arxiv.org/abs/2102.10592
Autor:
Yu, Hai-Tao, Meng, Dan, Feng, Meng-Xuan, Ruan, Kai-Yi, Dong, Jing-Jian, Bin-Shen, Xiao, Yan-Ping, Zhang, Xin-Hong, Shi, Li-Li, Jiang, Xiao-Hong
Publikováno v:
In Journal of Drug Delivery Science and Technology January 2024 91
Autor:
Yu, Hai-Tao
Deep neural networks has become the first choice for researchers working on algorithmic aspects of learning-to-rank. Unfortunately, it is not trivial to find the optimal setting of hyper-parameters that achieves the best ranking performance. As a res
Externí odkaz:
http://arxiv.org/abs/2008.13368
Autor:
Yu, Hai-Tao
Learning-to-rank has been intensively studied and has shown significantly increasing values in a wide range of domains. The performance of learning-to-rank methods is commonly evaluated using rank-sensitive metrics, such as average precision (AP) and
Externí odkaz:
http://arxiv.org/abs/2008.13373