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Graph Neural Networks (graph NNs) are a promising deep learning approach for analyzing graph-structured data.
A comprehensive survey on graph neural networks
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Dmitry Yarotsky · 2017
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng · 2013
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Open problem: The landscape of the loss surfaces of multilayer networks
Anna Choromanska, Yann LeCun, and Gérard Ben Arous · 2015
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P Adams · 2015
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Deep convolutional networks on graph-structured data
Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Universality of deep convolutional neural networks
Ding-Xuan Zhou · 2018
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Understanding generalization and optimization performance of deep CNNs
Pan Zhou and Jiashi Feng · 2018
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Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
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Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
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Break the ceiling: Stronger multi-scale deep graph convolutional networks
Sitao Luan, Mingde Zhao, Xiao-Wen Chang, and Doina Precup · 2019
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The generalization error of random features regression: Precise asymptotics and double descent curve
Song Mei and Andrea Montanari · 2019
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Revisiting graph neural networks: All we have is low-pass filters
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Approximation and non-parametric estimation of ResNet-type convolutional neural networks
Kenta Oono and Taiji Suzuki · 2019
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Chainer: A deep learning framework for accelerating the research cycle
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Stability and generalization of graph convolutional neural networks
Saurabh Verma and Zhi-Li Zhang · 2019
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How powerful are graph neural networks?
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Gresnet: Graph residuals for reviving deep graph neural nets from suspended animation
Jiawei Zhang · 2019
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