TensorFlow: Large-scale machine learning on heterogeneous systems, Oriol Vinyals, 2015. ,
Persistence images: a stable vector representation of persistent homology, Journal of Machine Learning Research, vol.18, issue.8, 2017. ,
The wave kernel signature: A quantum mechanical approach to shape analysis, IEEE International Conference on Computer Vision, pp.1626-1633, 2011. ,
Statistical topological data analysis using persistence landscapes, Journal of Machine Learning Research, vol.16, issue.77, pp.77-102, 2015. ,
sklearn-tda: a scikit-learn compatible python package for machine learning and tda, 2018. ,
Sliced Wasserstein kernel for persistence diagrams, International Conference on Machine Learning, vol.70, pp.664-673, 2017. ,
Local equivalence and intrinsic metrics between Reeb graphs, International Symposium on Computational Geometry, vol.77, p.15, 2017. ,
Stable topological signatures for points on 3d shapes, Computer Graphics Forum, vol.34, pp.1-12, 2015. ,
The structure and stability of persistence modules, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01330678
Stochastic convergence of persistence landscapes and silhouettes, Journal of Computational Geometry, vol.6, issue.2, pp.140-161, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-00923684
Spectral graph theory, 1997. ,
Extending persistence using Poincaré and Lefschetz duality, Foundations of Computational Mathematics, vol.9, issue.1, pp.79-103, 2009. ,
Computational topology: an introduction, 2010. ,
Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis, vol.30, pp.129-150, 2011. ,
DOI : 10.1016/j.acha.2010.04.005
URL : https://hal.archives-ouvertes.fr/inria-00541855
Deep learning with topological signatures, Advances in Neural Information Processing Systems, pp.1634-1644, 2017. ,
Stable and informative spectral signatures for graph matching, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2305-2312, 2014. ,
DOI : 10.1109/cvpr.2014.296
URL : http://arxiv.org/pdf/1304.1572
Adam: a method for stochastic optimization, 2014. ,
Persistence weighted Gaussian kernel for topological data analysis, International Conference on Machine Learning, vol.48, 2004. ,
Persistence Fisher kernel: a Riemannian manifold kernel for persistence diagrams, Advances in Neural Information Processing Systems, pp.10027-10038, 2018. ,
Persistence theory: from quiver representations to data analysis, 2015. ,
DOI : 10.1090/surv/209
URL : https://hal.archives-ouvertes.fr/hal-01247501
A stable multi-scale kernel for topological machine learning, IEEE Conference on Computer Vision and Pattern Recognition, 2015. ,
DOI : 10.1109/cvpr.2015.7299106
URL : http://arxiv.org/pdf/1412.6821
A concise and provably informative multiscale signature based on heat diffusion, Computer graphics forum, vol.28, pp.1383-1392, 2009. ,
DOI : 10.1111/j.1467-8659.2009.01515.x
URL : http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Sun09.pdf
, The GUDHI Project. GUDHI User and Reference Manual. GUDHI Editorial Board, 2015.
Scale-variant topological information for characterizing complex networks, 2018. ,
Netlsd: hearing the shape of a graph, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.2347-2356, 2018. ,
Hunt for the unique, stable, sparse and fast feature learning on graphs, Advances in Neural Information Processing Systems, pp.88-98, 2017. ,
How powerful are graph neural networks? arXiv, 2018. ,
Deep sets, Advances in Neural Information Processing Systems, pp.3391-3401, 2017. ,
Retgk: Graph kernels based on return probabilities of random walks, Advances in Neural Information Processing Systems, pp.3968-3978, 2018. ,