Graph-Based Representations in Pattern Recognition
portes grátis
Graph-Based Representations in Pattern Recognition
13th IAPR-TC-15 International Workshop, GbRPR 2023, Vietri sul Mare, Italy, September 6-8, 2023, Proceedings
Vento, Mario; Foggia, Pasquale; Carletti, Vincenzo; Conte, Donatello
Springer International Publishing AG
08/2023
184
Mole
Inglês
9783031427947
15 a 20 dias
Descrição não disponível.
Graph Kernels and Graph Algorithms.- Quadratic Kernel Learning for Interpolation Kernel Machine Based Graph Classification.- Minimum Spanning Set Selection in Graph Kernels.- Graph-based vs. Vector-based Classification: A Fair Comparison.- A Practical Algorithm for Max-Norm Optimal Binary Labeling of Graphs.- Efficient Entropy-based Graph Kernel.- Graph Neural Networks.- GNN-DES: A new end-to-end dynamic ensemble selection method based on multi-label graph neural network.- C2N-ABDP: Cluster-to-Node Attention-based Differentiable Pooling.- Splitting Structural and Semantic Knowledge in Graph Autoencodersfor Graph Regression.- Graph Normalizing Flows to Pre-image Free Machine Learning for Regression.- Matching-Graphs for Building Classification Ensembles.- Maximal Independent Sets for Pooling in Graph Neural Networks.- Graph-based Representations and Applications.- Detecting Abnormal Communication Patterns in IoT Networks Using Graph Neural Networks.- Cell segmentation of in situ transcriptomics data using signed graph partitioning.- Graph-based representation for multi-image super-resolution.- Reducing the Computational Complexity of the Eccentricity Transform.- Graph-Based Deep Learning on the Swiss River Network.
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graph algorithms;graph-based representation;structural pattern recognition;graph kernels;graph neural networks;graph clustering;graph matching;graph pooling;graph-based classifiers
Graph Kernels and Graph Algorithms.- Quadratic Kernel Learning for Interpolation Kernel Machine Based Graph Classification.- Minimum Spanning Set Selection in Graph Kernels.- Graph-based vs. Vector-based Classification: A Fair Comparison.- A Practical Algorithm for Max-Norm Optimal Binary Labeling of Graphs.- Efficient Entropy-based Graph Kernel.- Graph Neural Networks.- GNN-DES: A new end-to-end dynamic ensemble selection method based on multi-label graph neural network.- C2N-ABDP: Cluster-to-Node Attention-based Differentiable Pooling.- Splitting Structural and Semantic Knowledge in Graph Autoencodersfor Graph Regression.- Graph Normalizing Flows to Pre-image Free Machine Learning for Regression.- Matching-Graphs for Building Classification Ensembles.- Maximal Independent Sets for Pooling in Graph Neural Networks.- Graph-based Representations and Applications.- Detecting Abnormal Communication Patterns in IoT Networks Using Graph Neural Networks.- Cell segmentation of in situ transcriptomics data using signed graph partitioning.- Graph-based representation for multi-image super-resolution.- Reducing the Computational Complexity of the Eccentricity Transform.- Graph-Based Deep Learning on the Swiss River Network.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
