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Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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A new model for learning in graph domains
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Understanding the difficulty of training deep feedforward neural networks
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Adam: A method for stochastic optimization
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Neural combinatorial optimization with reinforcement learning
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Convolutional neural networks on graphs with fast localized spectral filtering
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2016
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Neural optimizer search with reinforcement learning
Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V. Le · 2017
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Quoc V. Le, and Alexey Kurakin · 2017
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Geniepath: Graph neural networks with adaptive receptive paths
Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, and Le Song · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 2018
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Regularized evolution for image classifier architecture search
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Snas: Stochastic neural architecture search
Sirui Xie, H P Zheng, Chunxiao Liu, and Liang Lin · 2018
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Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
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Large-scale learnable graph convolutional networks
Hongyang Gao, Zhengyang Wang, and Shuiwang Ji · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2018
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