| An introduction to topological data analysis: fundamental and practical aspects for data scientists F Chazal, B Michel Frontiers in artificial intelligence 4, 667963, 2021 | 769 | 2021 |
| The structure and stability of persistence modules F Chazal, V De Silva, M Glisse, S Oudot Springer 10, 978-3, 2016 | 652 | 2016 |
| Proximity of persistence modules and their diagrams F Chazal, D Cohen-Steiner, M Glisse, LJ Guibas, SY Oudot Proceedings of the twenty-fifth annual symposium on Computational geometry …, 2009 | 603 | 2009 |
| Persistence-based clustering in Riemannian manifolds F Chazal, LJ Guibas, SY Oudot, P Skraba Journal of the ACM (JACM) 60 (6), 1-38, 2013 | 386 | 2013 |
| Persistence stability for geometric complexes F Chazal, V De Silva, S Oudot Geometriae Dedicata 173 (1), 193-214, 2014 | 366 | 2014 |
| Gromov‐Hausdorff stable signatures for shapes using persistence F Chazal, D Cohen‐Steiner, LJ Guibas, F Mémoli, SY Oudot Computer Graphics Forum 28 (5), 1393-1403, 2009 | 362 | 2009 |
| Geometric inference for probability measures F Chazal, D Cohen-Steiner, Q Mérigot Foundations of Computational Mathematics 11, 733-751, 2011 | 317* | 2011 |
| The “λ-medial axis” F Chazal, A Lieutier Graphical models 67 (4), 304-331, 2005 | 260 | 2005 |
| A sampling theory for compact sets in Euclidean space F Chazal, D Cohen-Steiner, A Lieutier Proceedings of the twenty-second annual symposium on Computational geometry …, 2006 | 254 | 2006 |
| Towards persistence-based reconstruction in Euclidean spaces F Chazal, SY Oudot Proceedings of the twenty-fourth annual symposium on Computational geometry …, 2008 | 246 | 2008 |
| Perslay: A simple and versatile neural network layer for persistence diagrams M Carriere, F Chazal, IS Datashape, Y Ike, T Lacombe, M Royer, ... stat 1050, 5, 2019 | 243* | 2019 |
| Stochastic convergence of persistence landscapes and silhouettes F Chazal, BT Fasy, F Lecci, A Rinaldo, L Wasserman Proceedings of the thirtieth annual symposium on Computational geometry, 474-483, 2014 | 234 | 2014 |
| Geometric and topological inference JD Boissonnat, F Chazal, M Yvinec Cambridge University Press, 2018 | 222 | 2018 |
| Map-based exploration of intrinsic shape differences and variability RM Rustamov, M Ovsjanikov, O Azencot, M Ben-Chen, F Chazal, ... ACM Transactions on Graphics (TOG) 32 (4), 1-12, 2013 | 169 | 2013 |
| Robust topological inference: Distance to a measure and kernel distance B Fasy, F Lecci, L Wasserman Journal of Machine Learning Research 18 (159), 1-40, 2018 | 168 | 2018 |
| Persistence-based segmentation of deformable shapes P Skraba, M Ovsjanikov, F Chazal, L Guibas 2010 IEEE Computer Society Conference on Computer Vision and Pattern …, 2010 | 165 | 2010 |
| Molecular shape analysis based upon the Morse-Smale complex and the Connolly function F Cazals, F Chazal, T Lewiner Proceedings of the nineteenth annual symposium on Computational geometry …, 2003 | 153 | 2003 |
| Computing and processing correspondences with functional maps M Ovsjanikov, E Corman, M Bronstein, E Rodolà, M Ben-Chen, L Guibas, ... SIGGRAPH ASIA 2016 Courses, 1-60, 2016 | 152 | 2016 |
| Persistence-based structural recognition C Li, M Ovsjanikov, F Chazal Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 152 | 2014 |
| Subsampling methods for persistent homology F Chazal, BT Fasy, F Lecci, B Michel, A Rinaldo, L Wasserman International Conference on Machine Learning ICML 2015, 2014 | 149 | 2014 |
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