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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

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

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

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

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

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

Mesh representation matters:investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models: 《虚拟现实与智能硬件（中英文）》2024年第5期383-395,共13页Robert KOSK Richard SOUTHERN Lihua YOU Shaojun BIAN Willem KOKKE Greg MAGUIRE; Supported by the Centre for Digital Entertainment at Bournemouth University by the UK Engineering and Physical Sciences Research Council(EPSRC)EP/L016540/1 and Humain Ltd.; Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition sys...; 关键词：Shape modelling Deep 3D morphable models Representation learning Feature engineering Perceptual metrics

PUNet:A Semi-Supervised Anomaly Detection Model for Network Anomaly Detection Based on Positive Unlabeled Data: 《Computers, Materials & Continua》2024年第10期327-343,共17页Gang Long Zhaoxin Zhang; Network anomaly detection plays a vital role in safeguarding network security.However,the existing network anomaly detection task is typically based on the one-class zero-positive scenario.This approach is susceptible...; 关键词：Network anomaly detection representation learning candidate set CatBoost

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