references.bib

%% Spherical CNNs


@inproceedings{defferrard2019deepsphereequiv,
  title = {DeepSphere: towards an equivariant graph-based spherical CNN},
  author = {Defferrard, Micha\"el and Perraudin, Nathana\"el and Kacprzak, Tomasz and Sgier, Raphael},
  booktitle = {ICLR Workshop on Representation Learning on Graphs and Manifolds},
  year = {2019},
  url = {https://arxiv.org/abs/1904.05146},
}

@article{perraudin2019deepspherecosmo,
  title = {DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications},
  author = {Perraudin, Nathana\"el and Defferrard, Micha\"el and Kacprzak, Tomasz and Sgier, Raphael},
  journal = {Astronomy and Computing},
  year = {2019},
  url = {https://arxiv.org/abs/1810.12186},
}

@inproceedings{khasanova2017sphericalcnn,
  title = {Graph-based classification of omnidirectional images},
  author = {Khasanova, Renata and Frossard, Pascal},
  booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
  year = {2017},
  url = {https://arxiv.org/abs/1707.08301},
}

@inproceedings{cohen2018sphericalcnn,
  title = {Spherical CNNs},
  author = {Cohen, Taco S and Geiger, Mario and Koehler, Jonas and Welling, Max},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://arxiv.org/abs/1801.10130},
}

@inproceedings{esteves2018sphericalcnn,
  title = {Learning SO(3) Equivariant Representations with Spherical CNNs},
  author = {Esteves, Carlos and Allen-Blanchette, Christine and Makadia, Ameesh and Daniilidis, Kostas},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year = {2018},
  url = {https://arxiv.org/abs/1711.06721}
}

@inproceedings{boomsma2017sphericalcnn,
  title = {Spherical convolutions and their application in molecular modelling},
  author = {Boomsma, Wouter and Frellsen, Jes},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2017},
}

@inproceedings{su2017sphericalcnn,
  title = {Learning spherical convolution for fast features from 360 imagery},
  author = {Su, Yu-Chuan and Grauman, Kristen},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2017}
}

@inproceedings{coors2018sphericalcnn,
  author = {Benjamin Coors and Alexandru Paul Condurache and Andreas Geiger},
  title = {SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images},
  booktitle = {European Conference on Computer Vision},
  year = {2018}
}

% Not really a spherical CNN.
@article{schmelzle2017cosmological,
  title = {Cosmological model discrimination with Deep Learning},
  author = {Schmelzle, Jorit and Lucchi, Aurelien and Kacprzak, Tomasz and Amara, Adam and Sgier, Raphael and R{\'e}fr{\'e}gier, Alexandre and Hofmann, Thomas},
  journal = {arXiv:1707.05167},
  year = {2017}
}

@article{krachmalnicoff2019sphericalcnn,
  title = {Convolutional Neural Networks on the HEALPix sphere: a pixel-based algorithm and its application to CMB data analysis},
  author = {Krachmalnicoff, Nicoletta and Tomasi, Maurizio},
  journal = {arXiv:1902.04083},
  year = {2019},
}

@inproceedings{jiang2019sphericalcnn,
  title = {Spherical CNNs on Unstructured Grids},
  author = {Jiang, Chiyu "Max" and Huang, Jingwei and Kashinath, Karthik and Prabhat and Marcus, Philip and Niessner, Matthias},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://arxiv.org/abs/1901.02039},
}

@inproceedings{cohen2019gauge,
  title = {Gauge Equivariant Convolutional Networks and the Icosahedral CNN},
  author = {Cohen, Taco S. and Weiler, Maurice and Kicanaoglu, Berkay and Welling, Max},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {http://arxiv.org/abs/1902.04615},
}


%% Graph NNs


@article{bruna2013gnn,
  title = {Spectral Networks and Locally Connected Networks on Graphs},
  author = {Joan Bruna and Wojciech Zaremba and Arthur Szlam and Yann LeCun},
  journal = {arXiv:1312.6203},
  year = {2013},
  url = {https://arxiv.org/abs/1312.6203},
}

@inproceedings{defferrard2016graphnn,
  title = {Convolutional neural networks on graphs with fast localized spectral filtering},
  author = {Defferrard, Micha{\"e}l and Bresson, Xavier and Vandergheynst, Pierre},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2016},
  url = {https://arxiv.org/abs/1606.09375},
}

@inproceedings{boscaini2016anisotropicgraphnn,
  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},
  year = {2016},
}

@article{bronstein2017review,
  title = {Geometric deep learning: going beyond euclidean data},
  author = {Bronstein, Michael M and Bruna, Joan and LeCun, Yann and Szlam, Arthur and Vandergheynst, Pierre},
  journal = {IEEE Signal Processing Magazine},
  year = {2017},
}


%% NN stuff


@inproceedings{ioffe2015batchnorm,
  title = {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
  author = {Ioffe, Sergey and Szegedy, Christian},
  booktitle = {International Conference on Machine Learning},
  year = {2015},
}

@article{kingma2014adam,
  title = {Adam: A method for stochastic optimization},
  author = {Kingma, Diederik P and Ba, Jimmy},
  journal = {arXiv:1412.6980},
  year = {2014},
}


%% Spherical sampling schemes


@article{gorski2005healpix,
  title = {HEALPix: a framework for high-resolution discretization and fast analysis of data distributed on the sphere},
  author = {Gorski, Krzysztof M and Hivon, Eric and Banday, AJ and Wandelt, Benjamin D and Hansen, Frode K and Reinecke, Mstvos and Bartelmann, Matthia},
  journal = {The Astrophysical Journal},
  year = {2005},
}

@article{gorski1999healpixprimer,
  title = {The healpix primer},
  author = {Gorski, Krzysztof M and Wandelt, Benjamin D and Hansen, Frode K and Hivon, Eric and Banday, Anthony J},
  journal = {arXiv preprint astro-ph/9905275},
  year = {1999},
}

@article{baumgardner1985icosahedral,
  title = {Icosahedral discretization of the two-sphere},
  author = {Baumgardner, John R and Frederickson, Paul O},
  journal = {SIAM Journal on Numerical Analysis},
  year = {1985},
}

@article{driscoll1994fouriersphere,
  title = {Computing Fourier Transforms and Convolutions on the 2-Sphere},
  author = {Driscoll, J. R. and Healy, D. M.},
  journal = {Adv. Appl. Math.},
  year = {1994},
  url = {http://dx.doi.org/10.1006/aama.1994.1008},
}


%% Convergence and equivariance

@book{shubin,
  author = {Shubin, M. A.},
  title = {Pseudodifferential operators and spectral theory},
  publisher = {Springer-Verlag, Berlin},
  year = {2001},
  pages = {xii+288},
  edition = {Second},
  note = {Translated from the 1978 Russian original by Stig I. Andersson},
  url = {http://dx.doi.org/10.1007/978-3-642-56579-3},
  doi = {http://dx.doi.org/10.1007/978-3-642-56579-3}
}

@inproceedings{belkin2007convergence,
  title = {Convergence of Laplacian eigenmaps},
  author = {Belkin, Mikhail and Niyogi, Partha},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2007},
}

@article{belkin2005towards,
  title = {Towards a theoretical foundation for Laplacian-based manifold methods},
  author = {Belkin, Mikhail and Niyogi, Partha},
  journal = {Journal of Computer and System Sciences},
  year = {2008},
}


%% Data


@inproceedings{mudigonda2017climateevents,
  title = {Segmenting and tracking extreme climate events using neural networks},
  author = {Mudigonda, Mayur and Kim, Sookyung and Mahesh, Ankur and Kahou, Samira and Kashinath, Karthik and Williams, Dean and Michalski, Vincen and O’Brien, Travis and Prabhat, Mr},
  booktitle = {Deep Learning for Physical Sciences (DLPS) Workshop, held with NIPS Conference},
  year = {2017},
  url = {https://dl4physicalsciences.github.io/files/nips_dlps_2017_20.pdf}
}

@article{planck2015overview,
  title = {Planck 2015 results. I. Overview of products and scientific results},
  author = {{Planck Collaboration}},
  journal = {Astronomy \& Astrophysics},
  year = {2016},
}

@inproceedings{shrec17,
  title = {Large-scale 3D shape retrieval from ShapeNet Core55: SHREC'17 track},
  author = {Savva, Manolis and Yu, Fisher and Su, Hao and Kanezaki, Asako and Furuya, Takahiko and Ohbuchi, Ryutarou and Zhou, Zhichao and Yu, Rui and Bai, Song and Bai, Xiang and others},
  booktitle = {Eurographics Workshop on 3D Object Retrieval},
  year = {2017},
}

@article{shapenet,
  title = {Shapenet: An information-rich 3d model repository},
  author = {Chang, Angel X and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and others},
  journal = {arXiv:1512.03012},
  year = {2015},
}


% Cosmology


@article{schmelze2017cosmologicalmodel,
  title = {Cosmological model discrimination with Deep Learning},
  author = {{Schmelzle}, J. and {Lucchi}, A. and {Kacprzak}, T. and {Amara}, A. and {Sgier}, R. and {R{\'e}fr{\'e}gier}, A. and {Hofmann}, T.},
  journal = {arxiv:1707.05167},
  year = {2017},
}

@article{bartelman2010gravitationallensing,
    title = {Gravitational lensing},
   author = {{Bartelmann}, M.},
  journal = {Classical and Quantum Gravity},
     year = {2010},
}


% Software


@misc{pygsp,
  title = {PyGSP: Graph Signal Processing in Python},
  author = {Defferrard, Micha\"el and Martin, Lionel and Pena, Rodrigo and Perraudin, Nathana\"el},
  doi = {10.5281/zenodo.1003157},
  url = {https://github.com/epfl-lts2/pygsp/},
}

@article{scipy,
       author = {{Virtanen}, Pauli and {Gommers}, Ralf and {Oliphant},
         Travis E. and {Haberland}, Matt and {Reddy}, Tyler and
         {Cournapeau}, David and {Burovski}, Evgeni and {Peterson}, Pearu
         and {Weckesser}, Warren and {Bright}, Jonathan and {van der Walt},
         St{\'e}fan J.  and {Brett}, Matthew and {Wilson}, Joshua and
         {Jarrod Millman}, K.  and {Mayorov}, Nikolay and {Nelson}, Andrew
         R.~J. and {Jones}, Eric and {Kern}, Robert and {Larson}, Eric and
         {Carey}, CJ and {Polat}, {\.I}lhan and {Feng}, Yu and {Moore},
         Eric W. and {Vand erPlas}, Jake and {Laxalde}, Denis and
         {Perktold}, Josef and {Cimrman}, Robert and {Henriksen}, Ian and
         {Quintero}, E.~A. and {Harris}, Charles R and {Archibald}, Anne M.
         and {Ribeiro}, Ant{\^o}nio H. and {Pedregosa}, Fabian and
         {van Mulbregt}, Paul and {Contributors}, SciPy 1. 0},
        title = "{SciPy 1.0: Fundamental Algorithms for Scientific
                  Computing in Python}",
      journal = {Nature Methods},
         year = "2020",
       adsurl = {https://rdcu.be/b08Wh},
       doi = {https://doi.org/10.1038/s41592-019-0686-2},
}

@article{numpy,
  title={The NumPy array: a structure for efficient numerical computation},
  author={Walt, St{\'e}fan van der and Colbert, S Chris and Varoquaux, Gael},
  journal={Computing in Science \& Engineering},
  volume={13},
  number={2},
  pages={22--30},
  year={2011},
  publisher={IEEE Computer Society},
}

@misc{tensorflow,
  title={{TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
  url={https://www.tensorflow.org/},
  note={Software available from tensorflow.org},
  author={Mart\'{\i}n~Abadi and Ashish~Agarwal and Paul~Barham and Eugene~Brevdo and Zhifeng~Chen and Craig~Citro and Greg~S.~Corrado and Andy~Davis and Jeffrey~Dean and Matthieu~Devin and Sanjay~Ghemawat and Ian~Goodfellow and Andrew~Harp and Geoffrey~Irving and Michael~Isard and Yangqing Jia and Rafal~Jozefowicz and Lukasz~Kaiser and Manjunath~Kudlur and Josh~Levenberg and Dandelion~Man\'{e} and Rajat~Monga and Sherry~Moore and Derek~Murray and Chris~Olah and Mike~Schuster and Jonathon~Shlens and Benoit~Steiner and Ilya~Sutskever and Kunal~Talwar and Paul~Tucker and Vincent~Vanhoucke and Vijay~Vasudevan and Fernanda~Vi\'{e}gas and Oriol~Vinyals and Pete~Warden and Martin~Wattenberg and Martin~Wicke and Yuan~Yu and Xiaoqiang~Zheng},
  year={2015},
}

@article{matplotlib,
  Author    = {Hunter, J. D.},
  Title     = {Matplotlib: A 2D graphics environment},
  Journal   = {Computing in Science \& Engineering},
  Volume    = {9},
  Number    = {3},
  Pages     = {90--95},
  abstract  = {Matplotlib is a 2D graphics package used for Python for
  application development, interactive scripting, and publication-quality
  image generation across user interfaces and operating systems.},
  publisher = {IEEE COMPUTER SOC},
  doi       = {10.1109/MCSE.2007.55},
  year      = 2007,
}

@article{healpy,
  doi = {10.21105/joss.01298},
  url = {https://doi.org/10.21105/joss.01298},
  year = {2019},
  month = mar,
  publisher = {The Open Journal},
  volume = {4},
  number = {35},
  pages = {1298},
  author = {Andrea Zonca and Leo Singer and Daniel Lenz and Martin Reinecke and Cyrille Rosset and Eric Hivon and Krzysztof Gorski},
  title = {healpy: equal area pixelization and spherical harmonics transforms for data on the sphere in Python},
  journal = {Journal of Open Source Software},
}