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pro vyhledávání: '"Karl, W."'
Autor:
Pasini, Massimiliano Lupo, Choi, Jong Youl, Mehta, Kshitij, Zhang, Pei, Rogers, David, Bae, Jonghyun, Ibrahim, Khaled Z., Aji, Ashwin M., Schulz, Karl W., Polo, Jorda, Balaprakash, Prasanna
We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGNN expands the boundaries of gr
Externí odkaz:
http://arxiv.org/abs/2406.12909
