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检索条件"机构=Key Laboratory of Symbolic Computing and Knowledge Engineering"
1009 条 记 录,以下是951-960 订阅
排序:
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
arXiv
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arXiv 2022年
作者: Bao, Shilong Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Alibaba Group China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China Peng Cheng Laboratory China
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existin... 详细信息
来源: 评论
Msa-Net: A Medical Image Segmentation Network Based on Spatial Pyramid and Attention Mechanism
SSRN
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SSRN 2023年
作者: Feng, Yuncong Zhu, Xiaoyan Li, Yang Zhang, Xiaoli Lu, Huimin College of Computer Science and Engineering Changchun University of Technology Jilin Changchun130012 China Artificial Intelligence Research Institute Changchun University of Technology Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Academy for Advanced Interdisciplinary Studies Northeast Normal University Jilin Changchun130024 China Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI 3000 North Yuanda Avenue Gaoxin North District Changchun130102 China
Medical image segmentation is an important way to assist doctors to accurately diagnose diseases. However, semantic features are difficult to be fully extracted due to the complexity of medical image lesion tissue fea... 详细信息
来源: 评论
Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed
arXiv
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arXiv 2023年
作者: Xiao, Yubin Wang, Di Li, Boyang Wang, Mingzhao Wu, Xuan Zhou, Changliang Zhou, You Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University Singapore WeBank-NTU Joint Research Institute on Fintech Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of System Design and Intelligent Manufacturing Southern University of Science and Technology China
Neural construction models have shown promising performance for Vehicle Routing Problems (VRPs) by adopting either the Autoregressive (AR) or Non-Autoregressive (NAR) learning approach. While AR models produce high-qu... 详细信息
来源: 评论
Novel trusted authentication and evaluation framework in VANET
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Tongxin Xuebao/Journal on Communications 2009年 第1 A期30卷 107-113页
作者: Wu, Jing Liu, Yan-Heng Wang, Jian Li, Wei-Ping College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Changchun Jilin University Information Technologies Co. Ltd. Changchun 130012 China
Vehicular ad hoc networks (VANET) have many new characteristics unlike tranditional network, such as centriclessness, mobility and multi-hop transmission, which invalidate conventional key managements. The fast develo... 详细信息
来源: 评论
Fully-convolutional intensive feature flow neural network for text recognition
arXiv
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arXiv 2019年
作者: Zhang, Zhao Tang, Zemin Zhang, Zheng Wang, Yang Qin, Jie Wang, Meng School of Computer Science and Technology Soochow University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer and Information Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates
The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the tra... 详细信息
来源: 评论
CLDG: Contrastive Learning on Dynamic Graphs
arXiv
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arXiv 2024年
作者: Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China
The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph c... 详细信息
来源: 评论
CLDG: Contrastive Learning on Dynamic Graphs
CLDG: Contrastive Learning on Dynamic Graphs
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International Conference on Data engineering
作者: Yiming Xu Bin Shi Teng Ma Bo Dong Haoyi Zhou Qinghua Zheng Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China
The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph c...
来源: 评论
Human Eyes Move to the Target Earlier When Performing an Aiming Task with Increasing Difficulties
SSRN
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SSRN 2022年
作者: Liu, Xin Zhang, Yao Jiang, Xianta Zheng, Bin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Surgical Simulation Research Laboratory Department of Surgery University of Alberta EdmontonABT6G 2E1 Canada Department of Computing Science Memorial University of Newfoundland NewfoundlandA1B 3X5 Canada Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China
We examined whether human operators move their eyes earlier to a target before hands when the level of task difficulty increases. We hypothesized that subjects would perform less proactive eye movements in the difficu... 详细信息
来源: 评论
Group-attention Based Neural Machine Translation
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IOP Conference Series: Materials Science and engineering 2020年 第2期782卷
作者: Renyu Hu Hao Xu Yang Xiao Chenjun Wu Haiyang Jia College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China
Machine translation is a classic problem in natural language process (NLP). Recent years, the encoder and decoder through an attention mechanism has become a trend. Google proposed a new simple network architecture, t...
来源: 评论
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification
Learning Structured Twin-Incoherent Twin-Projective Latent D...
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IEEE International Conference on Data Mining (ICDM)
作者: Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Guangcan Liu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information and Control Nanjing University of Information Science and Technology Nanjing China
In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective L...
来源: 评论