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Graphs are one of the most important data structures for representing pairwise relations between objects.
Generalized finite-difference schemes
Blair Swartz and Burton Wendroff · 1969
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A neural device for searching direct correlations between structures and properties of chemical compounds
Igor I Baskin, Vladimir A Palyulin, and Nikolai S Zefirov · 1997
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Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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Rectified linear units improve restricted boltzmann machines
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Adam: A method for stochastic optimization
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Web-based integrated cloud cae platform for large-scale finite element analysis
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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A survey on deep learning advances on different 3d data representations
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Relational inductive biases, deep learning, and graph networks
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Matthias Fey and Jan E. Lenssen · 2019
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Abc: A big cad model dataset for geometric deep learning
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Hamiltonian graph networks with ode integrators
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Position-aware graph neural networks
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Learning to simulate and design for structural engineering
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