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REPRESENTATION: 作品数：930被引量：1111H指数：13; 导出分析报告; 相关作者：徐亮孙正兴张福炎熊洪允郝永华更多>>; 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>>

Learning Hierarchical Adaptive Code Clouds for Neural 3D Shape Representation: 《Machine Intelligence Research》2025年第2期304-323,共20页Yuanxun Lu Xinya Ji Hao Zhu Xun Cao; supported by the National Natural Science Foundation of China(Nos.62001213 and 62025108).; Neural implicit representation(NIR)has attracted significant attention in 3D shape representation for its efficiency,generalizability,and flexibility compared with traditional explicit representations.Previous works u...; 关键词：Representation learning shape analysis deep implicit function 3D reconstruction 3D modeling

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

Synergistic Multi-Drug Combination Prediction Based on Heterogeneous Network Representation Learning with Contrastive Learning: 《Tsinghua Science and Technology》2025年第1期215-233,共19页Xin Xi Jinhui Yuan Shan Lu Jieyue He; supported by National Key R&D Program of China(No.2019YFC1711000);Collaborative Innovation Center of Novel Software Technology and Industrialization.; The combination of multiple drugs is a significant therapeutic strategy that can enhance treatment effectiveness and reduce medication side effects.However,identifying effective synergistic drug combinations in a vast...; 关键词：western medicine synergistic drug combination heterogeneous network contrastive learning

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

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

Position-Aware and Subgraph Enhanced Dynamic Graph Contrastive Learning on Discrete-Time Dynamic Graph: 《Computers, Materials & Continua》2024年第11期2895-2909,共15页Jian Feng Tian Liu Cailing Du; Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of labels.However,existing studies neglect positional information ...; 关键词：Dynamic graph representation learning graph contrastive learning structure representation position representation evolving pattern

Deep-learning based representation and recognition for genome variants—from SNVs to structural variants: 《National Science Review》2024年第11期3-6,共4页Songbo Wang Kai Ye; supported by the National Natural Science Foundation of China(323B2015);K.Y.is supported by the National Natural Science Foundation of China(32125009);the National Key R&D Program of China(2022YFC3400300).; The evolution of genome sequencing and artificial intelligence(AI)has ushered in a new era of variant calling.Deep-learning methods have notably advanced the de-tection of both small-scale and large-scale variants,ove...; 关键词：REPRESENTATION Deep LEARNING

PointSmile:point self-supervised learning via curriculum mutual information: 《Science China(Information Sciences)》2024年第11期117-131,共15页Xin LI Mingqiang WEI Songcan CHEN; supported by National Natural Science Foundation of China(Grant Nos.T2322012,62172218,62032011);Shenzhen Science and Technology Program(Grant Nos.JCYJ20220818103401003,JCYJ20220530172403007);Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515010170)。; Self-supervised learning is attracting significant attention from researchers in the point cloud processing field.However,due to the natural sparsity and irregularity of point clouds,effectively extracting discriminat...; 关键词：PointSmile self-supervised learning curriculum mutual information point cloud representation learning

REPRESENTATION