Resource Type

Journal Article 387

Conference Videos 7

Year

2023 75

2022 76

2021 43

2020 48

2019 32

2018 24

2017 24

2016 15

2015 14

2014 1

2013 2

2012 2

2011 2

2010 2

2009 1

2008 1

2007 4

2006 1

2005 1

2003 1

open ︾

Keywords

Machine learning 50

Deep learning 36

machine learning 24

Reinforcement learning 15

deep learning 15

Artificial intelligence 14

artificial intelligence 8

Active learning 4

artificial neural network 4

Attention 3

Autonomous driving 3

Bayesian optimization 3

Big data 3

Sparse representation 3

Adaptive dynamic programming 2

Additive manufacturing 2

Adversarial attack 2

Autonomous learning 2

Autonomous vehicle 2

open ︾

Search scope:

排序： Display mode:

NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning Research Articles

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 409-421 doi: 10.1631/FITEE.2000657

Abstract: Recently, graph neural networks (GNNs) have achieved remarkable performance in representation learningon graph-structured data.To alleviate oversmoothing, we propose a nested graph network (NGAT), which can work in a semi-supervised

Keywords： Graph learning     Semi-supervised learning     Node classification     Attention    

HTML PDF Collect

The Group Interaction Field for Learning and Explaining Pedestrian Anticipation

Xueyang Wang,Xuecheng Chen,Puhua Jiang,Haozhe Lin,Xiaoyun Yuan,Mengqi Ji,Yuchen Guo,Ruqi Huang,Lu Fang,

Engineering doi: 10.1016/j.eng.2023.05.020

Abstract: Here, we propose the group interaction field (GIF), a novel group-aware representation that quantifiesGIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagationand graph attention that is adaptive to the group size and dynamic interaction states.

Keywords： Human behavior modeling and prediction     Implicit representation of pedestrian anticipation     Group interaction     Graph neural network    

HTML PDF Collect

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 488-497 doi: 10.1007/s11684-020-0762-0

Abstract: artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causality graph

Keywords： knowledge representation     uncertain     causality     graphical model     artificial intelligence     diagnosis     dyspnea    

HTML PDF Collect

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 527-544 doi: 10.1007/s11708-023-0880-x

Abstract: Data-based methods of supervised learning have gained popularity because of available Big Data and computingHowever, the common paradigm of the loss function in supervised learning requires large amounts of labeledTherefore, a fault detection method based on self-supervised feature learning was proposed to addressFirst, self-supervised learning was employed to extract features under various working conditions onlyThe self-supervised representation learning uses a sequence-based Triplet Loss.

Keywords： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear    

HTML PDF Collect

financially constrained small- and medium-sized enterprises based on a multi-relation translational graph Research Article

Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3,   Pages 388-402 doi: 10.1631/FITEE.2200151

Abstract: To address these challenges, we propose a graph neural network named Multi-relation tRanslatIonal GrapH

Keywords： Financing needs exploration     Graph representation learning     Transfer heterogeneity     Behavior heterogeneity    

HTML PDF Collect

Federated unsupervised representation learning Research Article

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1181-1193 doi: 10.1631/FITEE.2200268

Abstract: edge devices, we formulate a new problem in called federated unsupervised (FURL) to learn a common representationamong clients would make local models focus on different categories, leading to the inconsistency of representationrepresentations of samples from each client which can be shared with all clients for consistency of representationspace and an alignment module to align the representation of each client on a base model trained on

Keywords： Federated learning     Unsupervised learning     Representation learning     Contrastive learning    

HTML PDF Collect

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learningAnd the edge connections in the graph depend on the relationship between signals.On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced trainingeffectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graphfeature learning.

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

HTML PDF Collect

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1397-1414 doi: 10.1007/s11709-022-0860-y

Abstract: indices considering the severity of the ultimate collapse scenario are proposed using reinforcement learningand graph embedding.estimation of the Q values, and handle problems with different action spaces owing to utilization of graphmethods, it is demonstrated that the computational cost is considerably reduced because the reinforcement learningBesides, it is proved that the Q values produced by the reinforcement learning agent can make

Keywords： progressive collapse     alternate load path     demolition planning     reinforcement learning     graph embedding    

HTML PDF Collect

Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1046-1058 doi: 10.1631/FITEE.1500085

Abstract: With the development of face recognition using sparse representation based classification (SRC), manyHowever, when the dictionary is large and the representation is sparse, only a small proportion of theIn this paper, we employ a metric learning approach which helps find the active elements correctly byAfter the metric has been learned, a neighborhood graph is constructed in the projected space.A fast marching algorithm is used to rapidly select the subset from the graph, and SRC is implemented

Keywords： Face recognition     Sparse representation     Manifold structure     Metric learning     Subset selection    

HTML PDF Collect

Syntactic word embedding based on dependency syntax and polysemous analysis None

Zhong-lin YE, Hai-xing ZHAO

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 524-535 doi: 10.1631/FITEE.1601846

Abstract: To solve these problems, we propose an easy-to-use representation algorithm of syntactic word embeddingThe main procedures are: (1) A polysemous tagging algorithm is used for polysemous representation byWe conclude that SWE outperforms single embedding learning models.

Keywords： Dependency-based context     Polysemous word representation     Representation learning     Syntactic word embedding    

HTML PDF Collect

Classifying multiclass relationships between ASes using graph convolutional network

Frontiers of Engineering Management   Pages 653-667 doi: 10.1007/s42524-022-0217-1

Abstract: We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, to

Keywords： autonomous system     multiclass relationship     graph convolutional network     classification algorithm     Internet    

HTML PDF Collect

Laplacian sparse dictionary learning for image classification based on sparse representation Article

Fang LI, Jia SHENG, San-yuan ZHANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1795-1805 doi: 10.1631/FITEE.1600039

Abstract: Sparse representation is a mathematical model for data representation that has proved to be a powerfultool for solving problems in various fields such as pattern recognition, machine learning, and computerAs one of the building blocks of the sparse representation method, dictionary learning plays an importantOur method is based on manifold learning and double sparsity.We incorporate the Laplacian weighted graph in the sparse representation model and impose the l1-norm

Keywords： Sparse representation     Laplacian regularizer     Dictionary learning     Double sparsity     Manifold    

HTML PDF Collect

Self-supervised graph learning with target-adaptive masking for session-based recommendation Research Article

Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 73-87 doi: 10.1631/FITEE.2200137

Abstract: To tackle the above issues, we propose a self-supervised graph learning with (SGL-TM) method.Specifically, we first construct a global graph based on all involved sessions and subsequently capture

Keywords： Session-based recommendation     Self-supervised learning     Graph neural networks     Target-adaptive masking    

HTML PDF Collect

Erratum to: Latent discriminative representation learning for speaker recognition Erratum

Duolin Huang, Qirong Mao, Zhongchen Ma, Zhishen Zheng, Sidheswar Routray, Elias-Nii-Noi Ocquaye,2211708034@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,zhongchen_ma@ujs.edu.cn,1209103822@qq.com,sidheswar69@gmail.com,eocquaye@ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 914-914 doi: 10.1631/FITEE.19e0690

Abstract: Unfortunately the fifth author’s name was mis-spelt. It should be Sidheswar ROUTRAY.
HTML PDF Collect

Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments Research Article

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 117-130 doi: 10.1631/FITEE.2200073

Abstract: and remembering his/her own experience, we propose a novel network structure called the hierarchical graphSpecifically, we construct the multi-agent system as a graph, use a novel graph convolution structure

Keywords： Deep reinforcement learning     Graph-based communication     Maximum-entropy learning     Partial observability    

HTML PDF Collect

Title Author Date Type Operation

NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Journal Article

The Group Interaction Field for Learning and Explaining Pedestrian Anticipation

Xueyang Wang,Xuecheng Chen,Puhua Jiang,Haozhe Lin,Xiaoyun Yuan,Mengqi Ji,Yuchen Guo,Ruqi Huang,Lu Fang,

Journal Article

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

Journal Article

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Journal Article

financially constrained small- and medium-sized enterprises based on a multi-relation translational graph

Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Journal Article

Federated unsupervised representation learning

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Journal Article

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Journal Article

Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Journal Article

Syntactic word embedding based on dependency syntax and polysemous analysis

Zhong-lin YE, Hai-xing ZHAO

Journal Article

Classifying multiclass relationships between ASes using graph convolutional network

Journal Article

Laplacian sparse dictionary learning for image classification based on sparse representation

Fang LI, Jia SHENG, San-yuan ZHANG

Journal Article

Self-supervised graph learning with target-adaptive masking for session-based recommendation

Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn

Journal Article

Erratum to: Latent discriminative representation learning for speaker recognition

Duolin Huang, Qirong Mao, Zhongchen Ma, Zhishen Zheng, Sidheswar Routray, Elias-Nii-Noi Ocquaye,2211708034@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,zhongchen_ma@ujs.edu.cn,1209103822@qq.com,sidheswar69@gmail.com,eocquaye@ujs.edu.cn

Journal Article

Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Journal Article