Dongxu Yang

Nov 30, 2024 | mdpi.com | Lu Yao |Dongxu Yang |Zhe Jiang |Yi Liu

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Nov 6, 2024 | mdpi.com | Qinhua Wang |Dongxu Yang |Yi Tang |Xueshuang Yi

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.

Apr 3, 2024 | developer.nvidia.com | Dongxu Yang

Large-scale graph neural network (GNN) training presents formidable challenges, particularly concerning the scale and complexity of graph data. These challenges extend beyond the typical concerns of neural network forward and backward computations, encompassing issues such as bandwidth-intensive graph feature gathering and sampling, and the limitations of single GPU capacities.

Mar 8, 2024 | developer.nvidia.com | Dongxu Yang

Graph neural networks (GNNs) have revolutionized machine learning for graph-structured data. Unlike traditional neural networks, GNNs are good at capturing intricate relationships in graphs, powering applications from social networks to chemistry. They shine particularly in scenarios like node classification, where they predict labels for graph nodes, and link prediction, where they determine the presence of edges between nodes.

Mar 13, 2023 | mdpi.com | Hai Huang |Dongxu Yang |Mingan Lin

1. IntroductionThe area of the SE Tibetan Plateau contains the largest number of marine glaciers in China [1]. With the confirmation of global warming [2], these glaciers are experiencing significant shrinkage, which has increased year on year [3]. This has resulted in abundant numbers of large-scale glacial debris flows in the surrounding area [4].