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Graph neural networks (GNNs) have achieved high performance in analyzing graph-structured data and have been widely deployed in safety-critical areas, such as finance and autonomous driving.
Topology attack and defense for graph neural networks: An optimization perspective
Xu, K.; Chen, H.; Liu, S.; Chen, P.-Y.; Weng, T.-W.; Hong, M.; and Lin, X. 2019a · 1906
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Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
Boyd, S.; Parikh, N.; Chu, E.; Peleato, B.; and Eckstein, J. 2011 · 1935
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Explaining and Harnessing Adversarial Examples
Goodfellow, I.; Shlens, J.; and Szegedy, C. 2015 · 2015
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A General Analysis of the Convergence of ADMM
Nishihara, R.; Lessard, L.; Recht, B.; Packard, A.; and Jordan, M. 2015 · 2015
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Deep ADMM-Net for Compressive Sensing MRI
Yang, Y.; Sun, J.; Li, H.; and Xu, Z. 2016 · 2016
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Top-k Supervise Feature Selection via ADMM for Integer Programming
Fan, M.; Chang, X.; Zhang, X.; Wang, D.; and Du, L. 2017 · 2017
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Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2017 · 2017
Cited alongside, same era.
Adversarial attack on graph structured data
Dai, H.; Li, H.; Tian, T.; Huang, X.; Wang, L.; Zhu, J.; and Song, L. 2018 · 2018
Cited alongside, same era.
Extremely Low Bit Neural Network: Squeeze the Last Bit Out With ADMM
Leng, C.; Dou, Z.; Li, H.; Zhu, S.; and Jin, R. 2018 · 2018
Cited alongside, same era.
Hierarchical Graph Representation Learning with Differentiable Pooling
Ying, R.; You, J.; Morris, C.; Ren, X.; Hamilton, W. L.; and Leskovec, J. 2018 · 2018
Cited alongside, same era.
A Systematic DNN Weight Pruning Framework Using Alternating Direction Method of Multipliers
Zhang, T.; Ye, S.; Zhang, K.; Tang, J.; Wen, W.; Fardad, M.; and Wang, Y. 2018 · 2018
Cited alongside, same era.
Fast Graph Representation Learning with PyTorch Geometric
Fey, M.; and Lenssen, J. E. 2019 · 2019
Later among the works it cites.
Fast and Provable ADMM for Learning with Generative Priors
Gómez, F. L.; Eftekhari, A.; and Cevher, V. 2019 · 2019
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Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration
Poon, C.; and Liang, J. 2019 · 2019
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ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Methods of Multipliers
Ren, A.; Zhang, T.; Ye, S.; Li, J.; Xu, W.; Qian, X.; Lin, X.; and Wang, Y. 2019 · 2019
Later among the works it cites.
A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers
Yu, H. 2019 · 2019
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An ADMM Based Framework for AutoML Pipeline Configuration
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Adversarial attacks on neural networks for graph data
Zügner, D.; Akbarnejad, A.; and Günnemann, S. 2018 · 2018
Cited alongside, same era.
Adversarial attacks on graph neural networks via meta learning
Zügner, D.; and Günnemann, S. 2019 · 2018
Cited alongside, same era.
How Powerful are Graph Neural Networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2019b
Cited in the paper.
Liu, S.; Ram, P.; Vijaykeerthy, D.; Bouneffouf, D.; Bramble, G.; Samulowitz, H.; Wang, D.; Conn, A.; and Gray, A. G. 2020 · 2020
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