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}

@article{bronstein2017review,
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@article{hamilton2017review,
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}

% This one is over-selling.
@article{battaglia2018review,
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  archivePrefix = "arxiv",
  year={2018}
}

@article{ktena2018metriclearning,
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}

@inproceedings{parisot2017disease,
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  pages={177--185},
  year={2017},
  organization={Springer}
}

@article{seo2016gcrn,
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@article{li2018traffic,
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@inproceedings{monti2017recommendation,
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  pages={3697--3707},
  year={2017}
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@article{hop2018drugdesign,
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  journal={Molecular pharmaceutics},
  year={2018},
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}

@inproceedings{qi2017pointcloudsegmentation,
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  author={Qi, Xiaojuan and Liao, Renjie and Jia, Jiaya and Fidler, Sanja and Urtasun, Raquel},
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@inproceedings{baque2018shape,
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  year={2018}
}

@article{cohen2017convolutional,
  title={Convolutional networks for spherical signals},
  author={Cohen, Taco and Geiger, Mario and Welling, Max},
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  archivePrefix = "arxiv",
  year={2017}
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@article{cohen2018sphericalcnn,
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  archivePrefix = "arxiv",
  year={2018}
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@article{he2018cmbdl,
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@article{esteves2017sphericalcnn,
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  journal={arXiv:1711.06721},
  archivePrefix = "arxiv",
  year={2017}
}

@article{kondor2018clebsch,
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  journal={arXiv:1806.09231},
  archivePrefix = "arxiv",
  year={2018}
}

@inproceedings{coors2018spherenet,
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@inproceedings{su2017sphericalconv,
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@inproceedings{xiao2012recognizing,
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@inproceedings{zhang2014panocontext,
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  booktitle={European Conference on Computer Vision},
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  year={2014},
  organization={Springer}
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@inproceedings{masci2015gcnn,
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  booktitle={Proceedings of the IEEE international conference on computer vision workshops},
  pages={37--45},
  year={2015}
}

@inproceedings{boscaini2016acnn,
  title={Learning shape correspondence with anisotropic convolutional neural networks},
  author={Boscaini, Davide and Masci, Jonathan and Rodol{\`a}, Emanuele and Bronstein, Michael},
  booktitle={Advances in Neural Information Processing Systems},
  pages={3189--3197},
  year={2016}
}

@inproceedings{monti2017monet,
  title={Geometric deep learning on graphs and manifolds using mixture model CNNs},
  author={Monti, Federico and Boscaini, Davide and Masci, Jonathan and Rodola, Emanuele and Svoboda, Jan and Bronstein, Michael M},
  booktitle={Proc. CVPR},
  volume={1},
  number={2},
  pages={3},
  year={2017}
}

@article{he2018analysis,
  title={Analysis of Cosmic Microwave Background with Deep Learning},
  author={He, Siyu and Ravanbakhsh, Siamak and Ho, Shirley},
  year={2018}
}

@inproceedings{boomsma2017spherical,
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  booktitle={Advances in Neural Information Processing Systems},
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  year={2017}
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@article{shuman2013emerging,
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@article{hammond2011wavelets,
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@inproceedings{long2015fcn,
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@article{springenberg2014allconv,
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@article{le2018fgft,
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@article{gorski2005healpix,
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@article{reinecke2013libsharp,
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@article{ronchi1996cubed,
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@inproceedings{khasanova2017graphomni,
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@inproceedings{khasanova2017tigranet,
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@article{lin2013globalavgpooling,
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@inproceedings{li2015gatedgnn,
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@inproceedings{duvenaud2015gcn,
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@inproceedings{wang2016learnhist,
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@inproceedings{kalchbrenner2014dcnn,
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@article{kondor2018equivariance,
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@inproceedings{ioffe2015batchnorm,
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