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2020 – today
- 2023
[i16]Felix Hensel, Charles Arnal, Mathieu Carrière, Théo Lacombe, Hiroaki Kurihara, Yuichi Ike, Frédéric Chazal:
MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks. CoRR abs/2305.13271 (2023)- 2022
- [j6]Mathieu Carrière
, Bertrand Michel:
Statistical analysis of Mapper for stochastic and multivariate filters. J. Appl. Comput. Topol. 6(3): 331-369 (2022) - [i15]Thibault de Surrel, Felix Hensel, Mathieu Carrière, Théo Lacombe, Yuichi Ike, Hiroaki Kurihara, Marc Glisse, Frédéric Chazal:
RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds. CoRR abs/2202.01725 (2022) - [i14]David Loiseaux, Mathieu Carrière, Andrew J. Blumberg:
Efficient Approximation of Multiparameter Persistence Modules. CoRR abs/2206.02026 (2022) - 2021
- [j5]Ewan Carr, Mathieu Carrière, Bertrand Michel, Frédéric Chazal, Raquel Iniesta:
Identifying homogeneous subgroups of patients and important features: a topological machine learning approach. BMC Bioinform. 22(1): 449 (2021) - [c9]Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda:
Optimizing persistent homology based functions. ICML 2021: 1294-1303 - [c8]Théo Lacombe, Yuichi Ike, Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuhei Umeda:
Topological Uncertainty: Monitoring Trained Neural Networks through Persistence of Activation Graphs. IJCAI 2021: 2666-2672 - [i13]Théo Lacombe, Yuichi Ike, Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuhei Umeda:
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs. CoRR abs/2105.04404 (2021) - [i12]Michael Bleher, Lukas Hahn, Juan Ángel Patino-Galindo, Mathieu Carrière, Ulrich Bauer, Raul Rabadan, Andreas Ott:
Topology identifies emerging adaptive mutations in SARS-CoV-2. CoRR abs/2106.07292 (2021) - [i11]Jacob Leygonie, Mathieu Carrière, Théo Lacombe, Steve Oudot:
A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data Analysis. CoRR abs/2109.00530 (2021) - 2020
- [c7]Mathieu Carrière, Frédéric Chazal, Yuichi Ike, Théo Lacombe, Martin Royer, Yuhei Umeda:
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures. AISTATS 2020: 2786-2796 - [c6]Andrew Aukerman, Mathieu Carrière, Chao Chen, Kevin Gardner, Raúl Rabadán, Rami Vanguri:
Persistent Homology Based Characterization of the Breast Cancer Immune Microenvironment: A Feasibility Study. SoCG 2020: 11:1-11:20 - [c5]Mathieu Carrière, Andrew J. Blumberg:
Multiparameter Persistence Image for Topological Machine Learning. NeurIPS 2020 - [i10]Andrew J. Blumberg, Mathieu Carrière, Michael A. Mandell, Raul Rabadan, Soledad Villar:
MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data. CoRR abs/2001.01666 (2020) - [i9]Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan:
A note on stochastic subgradient descent for persistence-based functionals: convergence and practical aspects. CoRR abs/2010.08356 (2020)
2010 – 2019
- 2019
- [j4]Rachel Jeitziner, Mathieu Carrière, Jacques Rougemont, Steve Oudot, Kathryn Hess, Cathrin Brisken:
Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis. Bioinform. 35(18): 3339-3347 (2019) - [c4]Mathieu Carrière, Ulrich Bauer:
On the Metric Distortion of Embedding Persistence Diagrams into Separable Hilbert Spaces. SoCG 2019: 21:1-21:15 - [i8]Mathieu Carrière, Frédéric Chazal, Yuichi Ike, Théo Lacombe, Martin Royer, Yuhei Umeda:
A General Neural Network Architecture for Persistence Diagrams and Graph Classification. CoRR abs/1904.09378 (2019) - [i7]Mathieu Carrière, Bertrand Michel:
Approximation of Reeb spaces with Mappers and Applications to Stochastic Filters. CoRR abs/1912.10742 (2019) - 2018
- [j3]Mathieu Carrière, Steve Oudot:
Structure and Stability of the One-Dimensional Mapper. Found. Comput. Math. 18(6): 1333-1396 (2018) - [j2]Mathieu Carrière, Bertrand Michel, Steve Oudot:
Statistical Analysis and Parameter Selection for Mapper. J. Mach. Learn. Res. 19: 12:1-12:39 (2018) - [i6]Mathieu Carrière, Ulrich Bauer:
On the Metric Distortion of Embedding Persistence Diagrams into Reproducing Kernel Hilbert Spaces. CoRR abs/1806.06924 (2018) - [i5]Mathieu Carrière, Raul Rabadan:
Topological Data Analysis of Single-cell Hi-C Contact Maps. CoRR abs/1812.01360 (2018) - 2017
- [b1]Mathieu Carrière:
On metric and statistical properties of topological descriptors for geometric data. (Sur les propriétés métriques et statistiques des descripteurs topologiques pour les données géométriques). University of Paris-Sud, Orsay, France, 2017 - [c3]Mathieu Carrière, Steve Oudot:
Local Equivalence and Intrinsic Metrics between Reeb Graphs. SoCG 2017: 25:1-25:15 - [c2]Mathieu Carrière, Marco Cuturi, Steve Oudot:
Sliced Wasserstein Kernel for Persistence Diagrams. ICML 2017: 664-673 - [i4]Mathieu Carrière, Steve Oudot:
Local Equivalence and Intrinsic Metrics between Reeb Graphs. CoRR abs/1703.02901 (2017) - [i3]Mathieu Carrière, Bertrand Michel, Steve Oudot:
Statistical Analysis and Parameter Selection for Mapper. CoRR abs/1706.00204 (2017) - [i2]Mathieu Carrière, Marco Cuturi, Steve Oudot:
Sliced Wasserstein Kernel for Persistence Diagrams. CoRR abs/1706.03358 (2017) - 2016
- [c1]Mathieu Carrière, Steve Oudot:
Structure and Stability of the 1-Dimensional Mapper. SoCG 2016: 25:1-25:16 - 2015
- [j1]Mathieu Carrière, Steve Y. Oudot, Maks Ovsjanikov:
Stable Topological Signatures for Points on 3D Shapes. Comput. Graph. Forum 34(5): 1-12 (2015) - [i1]Mathieu Carrière, Steve Oudot:
Structure and Stability of the 1-Dimensional Mapper. CoRR abs/1511.05823 (2015)
Coauthor Index
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last updated on 2023-05-25 21:15 CEST by the dblp team
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