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Construction of Artificial Intelligence Practical Courses based on ModelArts 被引量:1
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作者 Jianrui Ding Guodong Xin +1 位作者 xuefeng piao Dongjie Zhu 《计算机教育》 2020年第12期144-150,共7页
With more and more colleges and universities set up artificial intelligence undergraduate major,the cultivation of artificial intelligence undergraduate has become a hot topic.The cultivation of AI undergraduates shou... With more and more colleges and universities set up artificial intelligence undergraduate major,the cultivation of artificial intelligence undergraduate has become a hot topic.The cultivation of AI undergraduates should draw on the successful experience of software engineering major,pay attention to cooperation with enterprises,and introduce case and project teaching.The paper presents one curriculum system of AI undergraduates major and practice courses based on Huawei’s ModelArts platform. 展开更多
关键词 AI software engineering modelArts
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MINE:A Method of Multi-Interaction Heterogeneous Information Network Embedding
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作者 Dongjie Zhu Yundong Sun +6 位作者 Xiaofang Li Haiwen Du Rongning Qu Pingping Yu xuefeng piao Russell Higgs Ning Cao 《Computers, Materials & Continua》 SCIE EI 2020年第6期1343-1356,共14页
Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do ... Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets. 展开更多
关键词 Network embedding network representation learning interactive network data mining
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