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We present the first differentiable Network Architecture Search (NAS) for Graph Neural Networks (GNNs).
Collective classification in network data
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ImageNet classification with deep convolutional neural networks
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Learning entity and relation embeddings for knowledge graph completion
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Scalable gradient-based tuning of continuous regularization hyperparameters
Luketina, J., Berglund, M., Greff, K., and Raiko, T · 2016
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Unrolled generative adversarial networks, 2016
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J · 2016
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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W. W., and Salakhutdinov, R · 2016
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Dilated residual networks
Yu, F., Koltun, V., and Funkhouser, T · 2017
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Interleaved group convolutions
Zhang, T., Qi, G.-J., Xiao, B., and Wang, J · 2017
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Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
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Probabilistic neural architecture search
Casale, F. P., Gordon, J., and Fusi, N · 2019
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
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GraphNAS: Graph neural architecture search with reinforcement learning
Gao, Y., Yang, H., Zhang, P., Zhou, C., and Hu, Y · 2019
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Lee, J., Lee, I., and Kang, J · 2019
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Predicting multicellular function through multi-layer tissue networks
Zitnik, M. and Leskovec, J · 2017
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Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
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Large-scale learnable graph convolutional networks
Gao, H., Wang, Z., and Ji, S · 2018
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Graph Attention Networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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SNAS: stochastic neural architecture search
Xie, S., Zheng, H., Liu, C., and Lin, L · 2018
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DARTS: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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MNASNet: Platform-aware neural architecture search for mobile
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V · 2019
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Deep graph library: Towards efficient and scalable deep learning on graphs
Wang, M., Yu, L., Zheng, D., Gan, Q., Gai, Y., Ye, Z., Li, M., Zhou, J., Huang, Q., Ma, C., Huang, Z., Guo, Q., Zhang, H., Lin, H., Zhao, J., Li, J., Smola, A. J., and Zhang, Z · 2019
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FBNET: Hardware-aware efficient convnet design via differentiable neural architecture search
Wu, B., Dai, X., Zhang, P., Wang, Y., Sun, F., Wu, Y., Tian, Y., Vajda, P., Jia, Y., and Keutzer, K · 2019
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Auto-GNN: Neural architecture search of graph neural networks
Zhou, K., Song, Q., Huang, X., and Hu, X · 2019
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