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Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency structure between data points.
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The link-prediction problem for social networks
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Scikit-learn: Machine learning in Python
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Learning constrained task similarities in graph-regularized multi-task learning
Semi-supervised classification with graph convolutional networks
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Flamary, R., Rakotomamonjy, A., and Gasso, G · 2014
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Dropout: a simple way to prevent neural networks from overfitting
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Gradient estimation using stochastic computation graphs
Schulman, J., Heess, N., Weber, T., and Abbeel, P · 2015
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A review of relational machine learning for knowledge graphs
Nickel, M., Murphy, K., Tresp, V., and Gabrilovich, E · 2016
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Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
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Molgan: An implicit generative model for small molecular graphs
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Hyperparameter optimization
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Bilevel programming for hyperparameter optimization and meta-learning
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Bilevel learning of the group lasso structure
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Backpropagation through the void: Optimizing control variates for black-box gradient estimation
Grathwohl, W., Choi, D., Wu, Y., Roeder, G., and Duvenaud, D · 2018
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Graphite: Iterative generative modeling of graphs
Grover, A., Zweig, A., and Ermon, S · 2018
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Neural relational inference for interacting systems
Kipf, T., Fetaya, E., Wang, K.-C., Welling, M., and Zemel, R · 2018
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Learning deep generative models of graphs
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., and Battaglia, P · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2018
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Graphrnn: A deep generative model for graphs
You, J., Ying, R., Ren, X., Hamilton, W. L., and Leskovec, J · 2018
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Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
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Monte carlo gradient estimation in machine learning, 2019
Mohamed, S., Rosca, M., Figurnov, M., and Mnih, A · 2019
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