Bai, S.[Song],
Zhang, F.H.[Fei-Hu],
Torr, P.H.S.[Philip H.S.],
Hypergraph convolution and hypergraph attention,
PR(110), 2021, pp. 107637.
Elsevier DOI
2011
Graph learning, Hypergraph learning, Graph neural networks,
Semi-supervised learning
BibRef
Gama, F.,
Isufi, E.,
Leus, G.,
Ribeiro, A.,
Graphs, Convolutions, and Neural Networks: From Graph Filters to
Graph Neural Networks,
SPMag(37), No. 6, November 2020, pp. 128-138.
IEEE DOI
2011
Convolution, Finite impulse response filters,
Autoregressive processes, Network topology, Information filters,
Graphical models
BibRef
Li, Y.S.[Yan-Sheng],
Chen, R.X.[Rui-Xian],
Zhang, Y.J.[Yong-Jun],
Zhang, M.[Mi],
Chen, L.[Ling],
Multi-Label Remote Sensing Image Scene Classification by Combining a
Convolutional Neural Network and a Graph Neural Network,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link
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BibRef
Jiang, J.J.[Jun-Jie],
He, Z.X.[Zai-Xing],
Zhang, S.Y.[Shu-You],
Zhao, X.Y.[Xin-Yue],
Tan, J.R.[Jian-Rong],
Learning to transfer focus of graph neural network for scene graph
parsing,
PR(112), 2021, pp. 107707.
Elsevier DOI
2102
Semantic relationship, Graphical focus, Scene graph,
Class imbalance, Image understanding
BibRef
Ruiz, L.[Luana],
Gama, F.[Fernando],
Ribeiro, A.[Alejandro],
Graph Neural Networks: Architectures, Stability, and Transferability,
PIEEE(109), No. 5, May 2021, pp. 660-682.
IEEE DOI
2105
Training, Stability analysis, Convolution, Neural networks,
Transforms, Strain, Probability distribution, Equivariance,
transferability
BibRef
Manessi, F.[Franco],
Rozza, A.[Alessandro],
Graph-based neural network models with multiple self-supervised
auxiliary tasks,
PRL(148), 2021, pp. 15-21.
Elsevier DOI
2107
Graph neural networks, Self-supervised learning,
Multi-task learning, Graph convolutional networks, Semi-supervised learning
BibRef
Wang, W.[Wei],
Gao, J.Y.[Jun-Yu],
Yang, X.S.[Xiao-Shan],
Xu, C.S.[Chang-Sheng],
Learning Coarse-to-Fine Graph Neural Networks for Video-Text
Retrieval,
MultMed(23), 2021, pp. 2386-2397.
IEEE DOI
2108
Feature extraction, Encoding, Task analysis, Semantics, Data models,
Cognition, Focusing, Video-text retrieval, graph neural network,
coarse-to-fine strategy
BibRef
Abadal, S.[Sergi],
Jain, A.[Akshay],
Guirado, R.[Robert],
Lopez-Alonso, J.[Jorge],
Alarcon, E.[Eduard],
Computing Graph Neural Networks: A Survey from Algorithms to
Accelerators,
Surveys(54), No. 9, October 2021, pp. xx-yy.
DOI Link
2112
Survey, Graph Neural Networks. Graph neural networks, GNN algorithms, graph embeddings, accelerators
BibRef
Tiezzi, M.[Matteo],
Marra, G.[Giuseppe],
Melacci, S.[Stefano],
Maggini, M.[Marco],
Deep Constraint-Based Propagation in Graph Neural Networks,
PAMI(44), No. 2, February 2022, pp. 727-739.
IEEE DOI
2201
Optimization, Computational modeling, Training,
Graph neural networks, Data models, Biological neural networks,
lagrangian optimization
BibRef
Ciano, G.[Giorgio],
Rossi, A.[Alberto],
Bianchini, M.[Monica],
Scarselli, F.[Franco],
On Inductive-Transductive Learning With Graph Neural Networks,
PAMI(44), No. 2, February 2022, pp. 758-769.
IEEE DOI
2201
Neural networks, Computational modeling, Training, Encoding,
Graph neural networks, Topology, Diffusion processes,
inductive learning
BibRef
Ding, J.Y.[Jing-Yi],
Cheng, R.[Ruohui],
Song, J.[Jian],
Zhang, X.R.[Xiang-Rong],
Jiao, L.C.[Li-Cheng],
Wu, J.[Jianshe],
Graph label prediction based on local structure characteristics
representation,
PR(125), 2022, pp. 108525.
Elsevier DOI
2203
Graph classification, Graph neural network,
Betweenness centrality node, Feature fusion, Characteristics representation
BibRef
Chen, Y.C.[Yu-Chi],
Lai, K.T.[Kuan-Ting],
Liu, D.[Dong],
Chen, M.S.[Ming-Syan],
TAGNet: Triplet-Attention Graph Networks for Hashtag Recommendation,
CirSysVideo(32), No. 3, March 2022, pp. 1148-1159.
IEEE DOI
2203
Feature extraction, Visualization, Social networking (online),
Correlation, Convolution, Fuses, Blogs, Hashtag recommendation,
attention mechanism
BibRef
Kan, S.C.[Shi-Chao],
Cen, Y.G.[Yi-Gang],
Li, Y.[Yang],
Vladimir, M.[Mladenovic],
He, Z.H.[Zhi-Hai],
Local Semantic Correlation Modeling Over Graph Neural Networks for
Deep Feature Embedding and Image Retrieval,
IP(31), 2022, pp. 2988-3003.
IEEE DOI
2205
Correlation, Graph neural networks, Measurement, Semantics,
Image retrieval, Training, Visualization, Deep feature embedding
BibRef
Thang, D.C.[Duong Chi],
Dat, H.T.[Hoang Thanh],
Tam, N.T.[Nguyen Thanh],
Jo, J.[Jun],
Hung, N.Q.V.[Nguyen Quoc Viet],
Aberer, K.[Karl],
Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks,
PRL(159), 2022, pp. 46-53.
Elsevier DOI
2206
graph neural networks, transferability
BibRef
Gao, H.Y.[Hong-Yang],
Ji, S.W.[Shui-Wang],
Graph U-Nets,
PAMI(44), No. 9, September 2022, pp. 4948-4960.
IEEE DOI
2208
Task analysis, Topology, Feature extraction,
Neural networks, Logic gates, Lattices, Graph neural networks, U-Net
BibRef
Wang, R.Z.[Run-Zhong],
Yan, J.C.[Jun-Chi],
Yang, X.K.[Xiao-Kang],
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment
Problem With Extension to Hypergraph and Multiple-Graph Matching,
PAMI(44), No. 9, September 2022, pp. 5261-5279.
IEEE DOI
2208
Pattern matching, Tensors, Splines (mathematics),
Feature extraction, Peer-to-peer computing, Optimization,
graph neural networks
BibRef
Tian, Y.[Yu],
Sun, X.[Xian],
Niu, R.G.[Rui-Gang],
Yu, H.F.[Hong-Feng],
Zhu, Z.C.[Zi-Cong],
Wang, P.[Peijin],
Fu, K.[Kun],
Fully-weighted HGNN: Learning efficient non-local relations with
hypergraph in aerial imagery,
PandRS(191), 2022, pp. 263-276.
Elsevier DOI
2208
Aerial imagery, Hypergraph neural networks,
Fully-weighted Hypergraph Neural Network (fully-weighted HGNN),
Hypergraph Convolutional Feature Pyramid Networks (hyper-FPN)
BibRef
Isufi, E.[Elvin],
Gama, F.[Fernando],
Ribeiro, A.[Alejandro],
EdgeNets: Edge Varying Graph Neural Networks,
PAMI(44), No. 11, November 2022, pp. 7457-7473.
IEEE DOI
2210
Convolution, Neural networks, Graph neural networks,
Computational complexity, Tools, Laplace equations, Edge varying,
learning on graphs
BibRef
Liu, M.[Meng],
Wang, Z.Y.[Zheng-Yang],
Ji, S.W.[Shui-Wang],
Non-Local Graph Neural Networks,
PAMI(44), No. 12, December 2022, pp. 10270-10276.
IEEE DOI
2212
Sorting, Task analysis, Graph neural networks, Convolution,
Aggregates, Nonhomogeneous media, Calibration, disassortative graphs
BibRef
Li, S.[Shuo],
Liu, F.[Fang],
Jiao, L.C.[Li-Cheng],
Chen, P.[Puhua],
Liu, X.[Xu],
Li, L.L.[Ling-Ling],
MFNet: A Novel GNN-Based Multi-Level Feature Network With Superpixel
Priors,
IP(31), 2022, pp. 7306-7321.
IEEE DOI
2212
Feature extraction, Task analysis, Object detection,
Image segmentation, Convolution, Graph neural networks, Shape,
representation learning
BibRef
Bouritsas, G.[Giorgos],
Frasca, F.[Fabrizio],
Zafeiriou, S.P.[Stefanos P.],
Bronstein, M.M.[Michael M.],
Improving Graph Neural Network Expressivity via Subgraph Isomorphism
Counting,
PAMI(45), No. 1, January 2023, pp. 657-668.
IEEE DOI
2212
Orbits, Message passing, Graph neural networks, Color,
Social networking (online), Proteins, Histograms, neural network expressivity
BibRef
Abdelaziz, I.[Ibrahim],
Crouse, M.[Maxwell],
Makni, B.[Bassem],
Austel, V.[Vernon],
Cornelio, C.[Cristina],
Ikbal, S.[Shajith],
Kapanipathi, P.[Pavan],
Makondo, N.[Ndivhuwo],
Srinivas, K.[Kavitha],
Witbrock, M.[Michael],
Fokoue, A.[Achille],
Learning to Guide a Saturation-Based Theorem Prover,
PAMI(45), No. 1, January 2023, pp. 738-751.
IEEE DOI
2212
Standards, Reinforcement learning, Graph neural networks,
Feature extraction, Benchmark testing, Search problems,
graph neural networks
BibRef
Xie, Y.C.[Yao-Chen],
Xu, Z.[Zhao],
Zhang, J.T.[Jing-Tun],
Wang, Z.Y.[Zheng-Yang],
Ji, S.W.[Shui-Wang],
Self-Supervised Learning of Graph Neural Networks: A Unified Review,
PAMI(45), No. 2, February 2023, pp. 2412-2429.
IEEE DOI
2301
Task analysis, Predictive models, Data models, Training,
Graph neural networks, Mutual information, Head, Deep learning,
unsupervised learning
BibRef
Chen, T.L.[Tian-Long],
Zhou, K.X.[Kai-Xiong],
Duan, K.Y.[Ke-Yu],
Zheng, W.Q.[Wen-Qing],
Wang, P.H.[Pei-Hao],
Hu, X.[Xia],
Wang, Z.Y.[Zhang-Yang],
Bag of Tricks for Training Deeper Graph Neural Networks:
A Comprehensive Benchmark Study,
PAMI(45), No. 3, March 2023, pp. 2769-2781.
IEEE DOI
2302
Training, Benchmark testing, Standards, Peer-to-peer computing,
Graph neural networks, Task analysis, Deep graph neural networks, benchmark
BibRef
Vasudevan, V.[Varun],
Bassenne, M.[Maxime],
Islam, M.T.[Md Tauhidul],
Xing, L.[Lei],
Image classification using graph neural network and multiscale
wavelet superpixels,
PRL(166), 2023, pp. 89-96.
Elsevier DOI
2302
Image classification, GNN, Multiscale superpixel, Wavelet
BibRef
Qian, S.S.[Sheng-Sheng],
Xue, D.[Dizhan],
Fang, Q.[Quan],
Xu, C.S.[Chang-Sheng],
Integrating Multi-Label Contrastive Learning With Dual Adversarial
Graph Neural Networks for Cross-Modal Retrieval,
PAMI(45), No. 4, April 2023, pp. 4794-4811.
IEEE DOI
2303
Semantics, Correlation, Data models, Task analysis,
Graph neural networks, Generative adversarial networks, Training
BibRef
Mohamed, H.A.[Hebatallah A.],
Pilutti, D.[Diego],
James, S.[Stuart],
del Bue, A.[Alessio],
Pelillo, M.[Marcello],
Vascon, S.[Sebastiano],
Locality-aware subgraphs for inductive link prediction in knowledge
graphs,
PRL(167), 2023, pp. 90-97.
Elsevier DOI
2303
Knowledge graphs, Inductive link prediction,
Graph neural networks, Local clustering, Personalized PageRank
BibRef
Kaczmarek, I.[Iwona],
Iwaniak, A.[Adam],
Swietlicka, A.[Aleksandra],
Classification of Spatial Objects with the Use of Graph Neural
Networks,
IJGI(12), No. 3, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Fan, X.L.[Xiao-Long],
Gong, M.[Maoguo],
Wu, Y.[Yue],
Markov clustering regularized multi-hop graph neural network,
PR(139), 2023, pp. 109518.
Elsevier DOI
2304
Graph data mining, Graph neural network,
Graph-level representation learning, Graph pattern recognition
BibRef
Hao, Y.J.[Yong-Jing],
Ma, J.[Jun],
Zhao, P.P.[Peng-Peng],
Liu, G.F.[Guan-Feng],
Xian, X.F.[Xue-Feng],
Zhao, L.[Lei],
Sheng, V.S.[Victor S.],
Multi-dimensional Graph Neural Network for Sequential Recommendation,
PR(139), 2023, pp. 109504.
Elsevier DOI
2304
Sequential Recommendation, Graph Neural Networks,
Self-attention Networks, Graph Embedding
BibRef
Wang, Z.Y.[Zheng-Yang],
Ji, S.W.[Shui-Wang],
Second-Order Pooling for Graph Neural Networks,
PAMI(45), No. 6, June 2023, pp. 6870-6880.
IEEE DOI
2305
Neural networks, Task analysis, Deep learning, Correlation, Covariance matrices,
Graph neural networks, graph pooling, second-order statistics
BibRef
Mueller, T.T.[Tamara T.],
Paetzold, J.C.[Johannes C.],
Prabhakar, C.[Chinmay],
Usynin, D.[Dmitrii],
Rueckert, D.[Daniel],
Kaissis, G.[Georgios],
Differentially Private Graph Neural Networks for Whole-Graph
Classification,
PAMI(45), No. 6, June 2023, pp. 7308-7318.
IEEE DOI
2305
Training, Privacy, Task analysis, Graph neural networks, Data models,
Stochastic processes, Image edge detection, Differential privacy,
graph neural networks
BibRef
Jiang, X.D.[Xiao-Dong],
Zhu, R.H.[Rong-Hang],
Ji, P.S.[Peng-Sheng],
Li, S.[Sheng],
Co-Embedding of Nodes and Edges With Graph Neural Networks,
PAMI(45), No. 6, June 2023, pp. 7075-7086.
IEEE DOI
2305
Task analysis, Convolution, Deep learning, Switches,
Image edge detection, Prediction algorithms, Graph embedding, link prediction
BibRef
Wan, H.[Hai],
Zhang, X.W.[Xin-Wei],
Zhang, Y.[Yubo],
Zhao, X.[Xibin],
Ying, S.[Shihui],
Gao, Y.[Yue],
Structure Evolution on Manifold for Graph Learning,
PAMI(45), No. 6, June 2023, pp. 7751-7763.
IEEE DOI
2305
Manifolds, Task analysis, Convolution, Data models,
Graph neural networks, Energy measurement, Correlation, graph energy
BibRef
Lyu, S.[Shuaiyi],
Wang, K.[Kai],
Zhang, L.[Liren],
Wang, B.L.[Bai-Ling],
Process-Oriented heterogeneous graph learning in GNN-Based ICS
anomalous pattern recognition,
PR(141), 2023, pp. 109661.
Elsevier DOI
2306
Fine-Grained anomaly recognition, Process-Oriented associativity,
Heterogeneous graph learning, Industrial control systems
BibRef
Gillioz, A.[Anthony],
Riesen, K.[Kaspar],
Graph Reduction Neural Networks for Structural Pattern Recognition,
SSSPR22(64-73).
Springer DOI
2301
BibRef
Seo, S.[Sangwoo],
Jung, S.[Seungjun],
Kim, C.[Changick],
Explanation-based Graph Neural Networks for Graph Classification,
ICPR22(2836-2842)
IEEE DOI
2212
Proteins, Analytical models, Machine learning,
Benchmark testing, Graph neural networks, Data models
BibRef
Wei, Z.[Ziyu],
Xiao, X.[Xi],
Zhang, B.[Bin],
Hu, G.W.[Guang-Wu],
Li, Q.[Qing],
Xia, S.T.[Shu-Tao],
Graph Data Augmentation for Node Classification,
ICPR22(4899-4905)
IEEE DOI
2212
Computational modeling, Benchmark testing, Graph neural networks, Topology
BibRef
Kim, J.[Jinwoo],
Oh, S.[Saeyoon],
Cho, S.J.[Sung-Jun],
Hong, S.[Seunghoon],
Equivariant Hypergraph Neural Networks,
ECCV22(XXI:86-103).
Springer DOI
2211
BibRef
Lin, W.[Wanyu],
Lan, H.[Hao],
Wang, H.[Hao],
Li, B.[Baochun],
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting
Graph Neural Networks,
CVPR22(13719-13728)
IEEE DOI
2210
Visualization, Privacy, Statistical analysis, Semantics,
Training data, Medical services, privacy and ethics in vision, Transparency
BibRef
Schaefer, S.[Simon],
Gehrig, D.[Daniel],
Scaramuzza, D.[Davide],
AEGNN: Asynchronous Event-based Graph Neural Networks,
CVPR22(12361-12371)
IEEE DOI
2210
Code, GNN.
WWW Link. Power demand, Object detection, Market research,
Graph neural networks, Pattern recognition, Object recognition,
Scene analysis and understanding
BibRef
Wu, H.Y.[Hong-Yan],
Guo, H.Y.[Hai-Yun],
Miao, Q.H.[Qing-Hai],
Huang, M.[Min],
Wang, J.Q.[Jin-Qiao],
Graph Neural Networks Based Multi-granularity Feature Representation
Learning for Fine-Grained Visual Categorization,
MMMod22(II:230-242).
Springer DOI
2203
BibRef
Zhao, G.M.[Gang-Ming],
Ge, W.F.[Wei-Feng],
Yu, Y.Z.[Yi-Zhou],
GraphFPN: Graph Feature Pyramid Network for Object Detection,
ICCV21(2743-2752)
IEEE DOI
2203
Representation learning, Image segmentation, Network topology,
Object detection, Feature extraction, Graph neural networks,
grouping and shape
BibRef
Xing, Y.F.[Yi-Fan],
He, T.[Tong],
Xiao, T.J.[Tian-Jun],
Wang, Y.X.[Yong-Xin],
Xiong, Y.J.[Yuan-Jun],
Xia, W.[Wei],
Wipf, D.[David],
Zhang, Z.[Zheng],
Soatto, S.[Stefano],
Learning Hierarchical Graph Neural Networks for Image Clustering,
ICCV21(3447-3457)
IEEE DOI
2203
Training, Couplings, Computational modeling, Predictive models,
Prediction algorithms, Graph neural networks, Faces,
Recognition and classification
BibRef
Liu, N.[Nian],
Zhao, W.[Wangbo],
Zhang, D.W.[Ding-Wen],
Han, J.W.[Jun-Wei],
Shao, L.[Ling],
Light Field Saliency Detection with Dual Local Graph Learning and
Reciprocative Guidance,
ICCV21(4692-4701)
IEEE DOI
2203
Fuses, Convolution, Computational modeling, Object detection,
Light fields, Graph neural networks,
Scene analysis and understanding
BibRef
Wang, T.T.[Tian-Tian],
Liu, S.[Sifei],
Tian, Y.[Yapeng],
Li, K.[Kai],
Yang, M.H.[Ming-Hsuan],
Video Matting via Consistency-Regularized Graph Neural Networks,
ICCV21(4882-4891)
IEEE DOI
2203
Training, Adaptation models, Computational modeling, Coherence,
Predictive models, Graph neural networks,
grouping and shape
BibRef
Jing, Y.C.[Yong-Cheng],
Yang, Y.D.[Yi-Ding],
Wang, X.C.[Xin-Chao],
Song, M.L.[Ming-Li],
Tao, D.C.[Da-Cheng],
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural
Networks,
ICCV21(5281-5290)
IEEE DOI
2203
Visualization, Adaptation models, Network topology,
Computational modeling, Aggregates, Transformers,
Vision applications and systems
BibRef
Li, X.Y.[Xin-Yi],
Ling, H.B.[Hai-Bin],
PoGO-Net: Pose Graph Optimization with Graph Neural Networks,
ICCV21(5875-5885)
IEEE DOI
2203
Training, Simultaneous localization and mapping, Pose estimation,
Benchmark testing, Cameras, Robustness, Graph neural networks,
Vision for robotics and autonomous vehicles
BibRef
Chen, H.K.[Hong-Kai],
Luo, Z.X.[Zi-Xin],
Zhang, J.H.[Jia-Hui],
Zhou, L.[Lei],
Bai, X.Y.[Xu-Yang],
Hu, Z.[Zeyu],
Tai, C.L.[Chiew-Lan],
Quan, L.[Long],
Learning to Match Features with Seeded Graph Matching Network,
ICCV21(6281-6290)
IEEE DOI
2203
Costs, Filtering, Message passing, Image matching,
Computer network reliability, Graph neural networks, Stereo,
Low-level and physics-based vision
BibRef
Arnab, A.[Anurag],
Sun, C.[Chen],
Schmid, C.[Cordelia],
Unified Graph Structured Models for Video Understanding,
ICCV21(8097-8106)
IEEE DOI
2203
Computational modeling, Message passing, Genomics, Cognition,
Graph neural networks, Task analysis,
Action and behavior recognition
BibRef
Fang, P.F.[Peng-Fei],
Harandi, M.[Mehrtash],
Petersson, L.[Lars],
Kernel Methods in Hyperbolic Spaces,
ICCV21(10645-10654)
IEEE DOI
2203
Geometry, Machine learning, Hilbert space,
Natural language processing, Graph neural networks,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Zeng, A.[Ailing],
Sun, X.[Xiao],
Yang, L.[Lei],
Zhao, N.X.[Nan-Xuan],
Liu, M.H.[Min-Hao],
Xu, Q.[Qiang],
Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation,
ICCV21(11416-11425)
IEEE DOI
2203
Representation learning, Deep learning, Codes, Pose estimation,
Graph neural networks, Gestures and body pose,
Representation learning
BibRef
Zhang, C.[Cheng],
Cui, Z.P.[Zhao-Peng],
Chen, C.[Cai],
Liu, S.C.[Shuai-Cheng],
Zeng, B.[Bing],
Bao, H.J.[Hu-Jun],
Zhang, Y.[Yinda],
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene
Context Graph and Relation-based Optimization,
ICCV21(12612-12621)
IEEE DOI
2203
Shape, Layout, Semantics, Predictive models, Linear programming,
Graph neural networks, 3D from a single image and shape-from-x,
Detection and localization in 2D and 3D
BibRef
Yew, Z.J.[Zi Jian],
Lee, G.H.[Gim Hee],
Learning Iterative Robust Transformation Synchronization,
3DV21(1206-1215)
IEEE DOI
2201
Analytical models, Message passing, Pipelines,
Graph neural networks, Synchronization, Iterative methods, registration
BibRef
Bahri, M.[Mehdi],
Bahl, G.[Gaétan],
Zafeiriou, S.P.[Stefanos P.],
Binary Graph Neural Networks,
CVPR21(9487-9496)
IEEE DOI
2111
Training, Schedules, Heuristic algorithms, Computational modeling,
Memory management, Process control, Benchmark testing
BibRef
Miyata, M.[Masaki],
Shiraki, K.[Katsutoshi],
Minoura, H.[Hiroaki],
Hirakawa, T.[Tsubasa],
Yamashita, T.[Takayoshi],
Fujiyoshi, H.[Hironobu],
Relational Subgraph for Graph-based Path Prediction,
MVA21(1-5)
DOI Link
2109
Prediction methods, Feature extraction
BibRef
Dominguez, M.[Miguel],
Ptucha, R.[Raymond],
Directional Graph Networks with Hard Weight Assignments,
ICPR21(7439-7446)
IEEE DOI
2105
Convolution, Computational modeling,
Neural networks, Robot sensing systems, Computational efficiency, Sensors
BibRef
Tian, Y.X.[Yu-Xing],
Liu, Z.[Zheng],
Liu, W.[Weiding],
Zhang, Z.[Zeyu],
Qu, Y.[Yanwen],
What nodes vote to? Graph classification without readout phase,
ICPR21(8439-8445)
IEEE DOI
2105
Message passing, Logic gates, Benchmark testing,
Feature extraction, Graph neural networks, Decoding,
graph neural networks
BibRef
Park, H.,
Jeong, M.,
Kim, Y.,
Kim, C.,
Self-Training Of Graph Neural Networks Using Similarity Reference For
Robust Training With Noisy Labels,
ICIP20(1951-1955)
IEEE DOI
2011
Training, Sampling methods, Noise measurement, Feature extraction,
Training data, Indexes, Data mining, Noisy label, sampling method,
graph-based CNN.
BibRef
Yu, C.Q.[Chang-Qian],
Liu, Y.F.[Yi-Fan],
Gao, C.X.[Chang-Xin],
Shen, C.H.[Chun-Hua],
Sang, N.[Nong],
Representative Graph Neural Network,
ECCV20(VII:379-396).
Springer DOI
2011
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Graph Convolutional Neural Networks .