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Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Finite difference methods for ordinary and partial differential equations: steady-state and time-dependent problems , volume 98
Randall J LeVeque · 2007
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Jeremy Kepner, Peter Aaltonen, David Bader, Aydin Buluç, Franz Franchetti, John Gilbert, Dylan Hutchison, Manoj Kumar, Andrew Lumsdaine, Henning Meyerhenke, et al · 2016
Earlier work this paper cites.
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https://github.com/dmlc/dlpack , 2017
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Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Graph convolutional neural networks for web-scale recommender systems
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Attention is all you need
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Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Modeling polypharmacy side effects with graph convolutional networks
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