Abstract: Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. In recent years, numerous researchers have ...
The mathematics behind artificial intelligence (AI) and machine learning (ML) rely on linear algebra, calculus, probability, ...
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The early minds behind the machine: Founders of artificial intelligenceTuring's 1950 paper didn't just pose the profound question, "Can machines think?". It ignited a quest to build AI technology ...
Just when you thought the pace of change of AI models couldn’t get any faster, it accelerates yet again. In the popular news media, the introduction of DeepSeek in January 2025 created a moment that ...
The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the ...
To address these issues, we propose a new GNN algorithm, LEGNN (Local and Global Enhanced Graph Neural Network), which introduces several key improvements over traditional GNN models such as GraphSAGE ...
we propose a Spatio-Temporal Multi-Graphs Convolutional Network (STMGCN), which collects information about the social interactions of pedestrians in a crowd by focusing on extracting the largest ...
cGCN on the graph-represented data can be extended to fMRI data ... Lebo, et al. "Application of convolutional recurrent neural network for individual recognition based on resting state fmri data." ...
We propose a supervised data-driven method to predict S-wave velocity using a graph convolutional network with a bidirectional ... the prediction performance is increased by an unsupervised graph ...
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