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REPRESENTATION: 作品数：925被引量：1104H指数：13; 导出分析报告; 相关作者：徐亮孙正兴张福炎熊洪允郑运平更多>>; 相关机构：中国科学院大学浙江大学西安外国语大学华中师范大学更多>>; 相关期刊：更多>>; 相关基金：国家自然科学基金国家重点基础研究发展计划国家教育部博士点基金国家高技术研究发展计划更多>>

GTE:learning code AST representation efficiently and effectively: 《Science China(Information Sciences)》2025年第3期389-390,共2页Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO; With the development of deep learning in recent years,code representation learning techniques have become the foundation of many software engineering tasks such as program classification[1]and defect detection.Earlier...; 关键词：SUCH LEARNING REPRESENTATION

Ontology Matching Method Based on Gated Graph Attention Model: 《Computers, Materials & Continua》2025年第3期5307-5324,共18页Mei Chen Yunsheng Xu Nan Wu Ying Pan; supported by the National Natural Science Foundation of China(grant numbers 62267005 and 42365008);the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing.; With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms o...; 关键词：Ontology matching representation learning OWL2Vec*method graph attention model

FDCPNet:feature discrimination and context propagation network for 3D shape representation: 《虚拟现实与智能硬件(中英文)》2025年第1期83-94,共12页Weimin SHI Yuan XIONG Qianwen WANG Han JIANG Zhong ZHOU; Supported by the National Key R&D Program of China(2022YFC3803600).; Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or ...; 关键词：3D shape representation Mesh model MeshNet Feature discrimination Context propagation

Deep learning-based software engineering:progress,challenges,and opportunities: 《Science China(Information Sciences)》2025年第1期54-141,共88页Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG; Researchers have recently achieved significant advances in deep learning techniques,which in turn has substantially advanced other research disciplines,such as natural language processing,image processing,speech recog...; 关键词：deep learning software engineering software benchmark software artifact representation survey

Subspace Clustering via Block-Diagonal Decomposition: 《Chinese Journal of Electronics》2024年第6期1373-1382,共10页Zhiqiang FU Yao ZHAO Dongxia CHANG Yiming WANG; supported by the National Key R&D Program of China(Grant No.2021ZD0112100);the National Natural Science Fundation of China(Grant No.62120106009);the Fundamental Research Funds for the Central Universities(Grant No.2022JBZY043)。; The subspace clustering has been addressed by learning the block-diagonal self-expressive matrix.This block-diagonal structure heavily affects the accuracy of clustering but is rather challenging to obtain.A novel and...; 关键词：Subspace clustering Representation matrix Low-rank representation

Knowledge Graph Completion Method of Combining Structural Information with Semantic Information: 《Chinese Journal of Electronics》2024年第6期1412-1420,共9页Binhao HU Jianpeng ZHANG Hongchang CHEN; supported by the National Natural Science Foundation of China(Grant No.62002384);the General Project of China Postdoctoral Science Foundation(Grant No.2020M683760);the Songshan Laboratory Project(Grant No.221100210700-03)。; With the development of knowledge graphs,a series of applications based on knowledge graphs have emerged.The incompleteness of knowledge graphs makes the effect of the downstream applications affected by the quality o...; 关键词：Knowledge graph Knowledge graph completion Representation learning

Representation strategy for unsupervised domain adaptation on person re-identification: 《Optoelectronics Letters》2024年第12期749-756,共8页LI Hao ZHANG Tao LI Shuang LI Xuan ZHAO Xin; The task of unsupervised person re-identification(Re-ID)is to transfer the knowledge learned in the source domain with no labels to the target domain with no labels.Due to the significant differences in the background...; 关键词：NETWORKS branch branching

A general tail item representation enhancement framework for sequential recommendation被引量：1: 《Frontiers of Computer Science》2024年第6期137-148,共12页Mingyue CHENG Qi LIU Wenyu ZHANG Zhiding LIU Hongke ZHAO Enhong CHEN; the National Key R&D Program of China(No.2021YFF0901003)。; Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems(SRS).However,the current deep model structures are limited in their ability to learn high-...; 关键词：sequential recommendation long-tail distribution training accelerating

Federated learning-outcome prediction with multi-layer privacy protection被引量：1: 《Frontiers of Computer Science》2024年第6期205-214,共10页Yupei ZHANG Yuxin LI Yifei WANG Shuangshuang WEI Yunan XU Xuequn SHANG; the National Natural Science Foundation of China(Grant Nos.62272392,U1811262,61802313);the Key Research and Development Program of China(2020AAA0108500);the Key Research and Development Program of Shaanxi Province(2023-YBGY-405);the Fundamental Research Funds for the Central University(D5000230088);the Higher Research Funding on International Talent Cultivation at NPU(GJGZZD202202)。; Learning-outcome prediction(LOP)is a long-standing and critical problem in educational routes.Many studies have contributed to developing effective models while often suffering from data shortage and low generalizatio...; 关键词：federated learning local subspace learning hierarchical privacy protection learning outcome prediction privacy-protected representation learning

Federated Local Compact Representation Communication:Framework and Application: 《Machine Intelligence Research》2024年第6期1103-1120,共18页Zhengquan Luo Yunlong Wang Zilei Wang; National Natural Science Foundation of China(Nos.62176246,61836008,62006225,61906199 and 62071468);Strategic Priority Research Program of Chinese Academy of Sciences(CAS),China(No.XDA27040700);Beijing Nova Program,China(Nos.Z201100006820050 and Z211100002121010).; The core of federated learning(FL)is to transfer data diversity and distribution knowledge of cross-client domains.Al-though adopted by most FL methods,model-sharing-based communication has limitations such as unstabl...; 关键词：Federated learning representation learning domain adaptation BIOMETRICS iris recognition

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