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pro vyhledávání: '"LaSalle, Dominique"'
Training large scale Graph Neural Networks (GNNs) requires significant computational resources, and the process is highly data-intensive. One of the most effective ways to reduce resource requirements is minibatch training coupled with graph sampling
Externí odkaz:
http://arxiv.org/abs/2310.12403
Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection, and search. In these domains, the graphs are typically large and heter
Externí odkaz:
http://arxiv.org/abs/2112.15345
Autor:
LaSalle, Dominique, Karypis, George
Publikováno v:
In Journal of Parallel and Distributed Computing February 2015 76:66-80
Autor:
LaSalle, Dominique, Karypis, George
Publikováno v:
In Parallel Computing December 2014 40(10):754-767
Autor:
LaSalle, Dominique
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstructured data. GNNs pose several computational challenges that are not present in neural networks for structured data such as images or sequences. While G
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ad47778902ead6f59b4acfeb6e919dc
Autor:
LaSalle, Dominique, Karypis, George
Publikováno v:
Euro-Par 2015: Parallel Processing; 2015, p467-478, 12p
Autor:
Lasalle, Dominique, Karypis, George
Publikováno v:
2013 IEEE 27th International Symposium on Parallel & Distributed Processing; 2013, p225-236, 12p