Results 81 to 90 of about 1,708,308 (346)
Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
, 2023 One of the primary objectives of truss structure design optimization is to minimize the total weight by determining the optimal sizes of the truss members while ensuring structural stability and integrity against external loads.
Mamdouh El-Badry +2 more
core +1 more source
CoRR, 2019 Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks. Recently, graph neural networks have been successfully applied to graph structured data such as point cloud and molecular data.
Zhen Zhang 0008 +2 more
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Modality Fusion Vision Transformer for Hyperspectral and LiDAR Data Collaborative Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingIn recent years, collaborative classification of multimodal data, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR), has been widely used to improve remote sensing image classification accuracy.
Bin Yang +5 more
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Clenshaw Graph Neural Networks
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023 Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational methods for learning graph representations. Recent GCN models use various residual connection techniques to alleviate the model degradation problem such as over-smoothing and gradient vanishing.
Yuhe Guo, Zhewei Wei
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Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge Classification
, 2023 Graph neural networks (GNNs) have shown great ability in modeling graphs; however, their performance would significantly degrade when there are noisy edges connecting nodes from different classes.
Shao, M +4 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Graph Neural Networks (GNNs) have shown advantages in various graph-based applications. Most existing GNNs assume strong homophily of graph structure and apply permutation-invariant local aggregation of neighbors to learn a representation for each node. However, they fail to generalize to heterophilic graphs, where most neighboring nodes have different
Tianmeng Yang +5 more
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Studying the capacity of cellular encoding to generate feedforward neural network topologies [PDF]
, 2004 Proceeding of: IEEE International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 25-29 July 2004Many methods to codify artificial neural networks have been developed to avoid the disadvantages of direct encoding schema, improving the search ...
Gutiérrez Sánchez, Germán +3 more
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k-Nearest Neighbor Learning with Graph Neural Networks
Mathematics, 2021 k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance ...
Seokho Kang
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scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
Nature Communications, 2021 Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex ...
Juexin Wang +9 more
semanticscholar +1 more source
DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions
, 2020 Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP). We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution ...
Filipp, Nikitin +2 more
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