Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Xie, Chenhao"'
Publikováno v:
Data Intelligence, Vol 1, Iss 3, Pp 271-288 (2019)
Knowledge base plays an important role in machine understanding and has been widely used in various applications, such as search engine, recommendation system and question answering. However, most knowledge bases are incomplete, which can cause many
Externí odkaz:
https://doaj.org/article/d95a3c822cc7410082dc28b60660cd5f
Autor:
Chen, Jieyang, Xie, Chenhao, Firoz, Jesun S, Li, Jiajia, Song, Shuaiwen Leon, Barker, Kevin, Raugas, Mark, Li, Ang
Sparse linear algebra kernels play a critical role in numerous applications, covering from exascale scientific simulation to large-scale data analytics. Offloading linear algebra kernels on one GPU will no longer be viable in these applications, simp
Externí odkaz:
http://arxiv.org/abs/2209.07552
Autor:
Geng, Tong, Wu, Chunshu, Zhang, Yongan, Tan, Cheng, Xie, Chenhao, You, Haoran, Herbordt, Martin C., Lin, Yingyan, Li, Ang
Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three years. Compared with other deep learning modalities, high-performance hardware acceleration of GCNs is as critical but even more challenging. The hurdles arise from
Externí odkaz:
http://arxiv.org/abs/2203.03606
Autor:
Chen, Lihan, Jiang, Sihang, Liu, Jingping, Wang, Chao, Zhang, Sheng, Xie, Chenhao, Liang, Jiaqing, Xiao, Yanghua, Song, Rui
Publikováno v:
KNOSYS_108371, 2022
Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community. Many solutions have been proposed for the rule mining f
Externí odkaz:
http://arxiv.org/abs/2202.10381
Autor:
Liu, Jingping, Tian, Xianyang, Tong, Hanwen, Xie, Chenhao, Ruan, Tong, Cong, Lin, Wu, Baohua, Wang, Haofen
Publikováno v:
In Information Processing and Management July 2024 61(4)
Distantly supervision automatically generates plenty of training samples for relation extraction. However, it also incurs two major problems: noisy labels and imbalanced training data. Previous works focus more on reducing wrongly labeled relations (
Externí odkaz:
http://arxiv.org/abs/2105.10158
High Quality Mobile Virtual Reality (VR) is what the incoming graphics technology era demands: users around the world, regardless of their hardware and network conditions, can all enjoy the immersive virtual experience. However, the state-of-the-art
Externí odkaz:
http://arxiv.org/abs/2102.13191
Autor:
Xie, Chenhao, Chen, Jieyang, Firoz, Jesun S, Li, Jiajia, Song, Shuaiwen Leon, Barker, Kevin, Raugas, Mark, Li, Ang
Designing efficient and scalable sparse linear algebra kernels on modern multi-GPU based HPC systems is a daunting task due to significant irregular memory references and workload imbalance across the GPUs. This is particularly the case for Sparse Tr
Externí odkaz:
http://arxiv.org/abs/2012.06959
Autor:
Tan, Cheng, Xie, Chenhao, Geng, Tong, Marquez, Andres, Tumeo, Antonino, Barker, Kevin, Li, Ang
The next generation HPC and data centers are likely to be reconfigurable and data-centric due to the trend of hardware specialization and the emergence of data-driven applications. In this paper, we propose ARENA -- an asynchronous reconfigurable acc
Externí odkaz:
http://arxiv.org/abs/2011.04931
People learn to discriminate between classes without explicit exposure to negative examples. On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true pred
Externí odkaz:
http://arxiv.org/abs/2005.03228