Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Benjamin Charlier"'
Autor:
Igor Koval, Alexandre Bône, Maxime Louis, Thomas Lartigue, Simona Bottani, Arnaud Marcoux, Jorge Samper-González, Ninon Burgos, Benjamin Charlier, Anne Bertrand, Stéphane Epelbaum, Olivier Colliot, Stéphanie Allassonnière, Stanley Durrleman
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
Abstract Alzheimer’s disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognitive impairmen
Externí odkaz:
https://doaj.org/article/3bb22a9b97884f2c9673b8a79351bb74
Autor:
Sieun Lee, Morgan L. Heisler, Karteek Popuri, Nicolas Charon, Benjamin Charlier, Alain Trouvé, Paul J. Mackenzie, Marinko V. Sarunic, Mirza Faisal Beg
Publikováno v:
Frontiers in Neuroscience, Vol 11 (2017)
Optical coherence tomography provides high-resolution 3D imaging of the posterior segment of the eye. However, quantitative morphological analysis, particularly relevant in retinal degenerative diseases such as glaucoma, has been confined to simple s
Externí odkaz:
https://doaj.org/article/5965c376c60342a293b1be3183efb23d
Autor:
Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré La Tour, Ghislain Durif, Dantas, Cassio F., Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoit Malézieux, Badr Moufad, Nguyen, Binh T., Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter
Publikováno v:
NeurIPS 2022-36th Conference on Neural Information Processing Systems
NeurIPS 2022-36th Conference on Neural Information Processing Systems, Nov 2022, New Orleans, United States
HAL
NeurIPS 2022-36th Conference on Neural Information Processing Systems, Nov 2022, New Orleans, United States
HAL
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several challenges: rese
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c27af824eab5b9e93e2abfda672a586
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR 2021-IEEE/CVF Conference on Computer Vision and Pattern Recognition
CVPR 2021-IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2021, Virtual event, United States. pp.12905-12913
CVPR
CVPR 2021-IEEE/CVF Conference on Computer Vision and Pattern Recognition
CVPR 2021-IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2021, Virtual event, United States. pp.12905-12913
CVPR
International audience; A natural way to model the evolution of an object (growth of a leaf for instance) is to estimate a plausible deforming path between two observations. This interpolation process can generate deceiving results when the set of co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39fdbb91ddda01655b6a9706e938dcff
https://hal.archives-ouvertes.fr/hal-03251752/document
https://hal.archives-ouvertes.fr/hal-03251752/document
Transporting Deformations of Face Emotions in the Shape Spaces: A Comparison of Different Approaches
Autor:
Stanley Durrleman, Franco Milicchio, Luciano Teresi, Valerio Varano, Maxime Louis, Benjamin Charlier, Paolo Piras, Antonio Profico
Publikováno v:
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision, Springer Verlag, 2021, ⟨10.1007/s10851-021-01030-6⟩
Journal of Mathematical Imaging and Vision, 2021, ⟨10.1007/s10851-021-01030-6⟩
Journal of Mathematical Imaging and Vision, Springer Verlag, 2021, ⟨10.1007/s10851-021-01030-6⟩
Journal of Mathematical Imaging and Vision, 2021, ⟨10.1007/s10851-021-01030-6⟩
Studying the changes of shape is a common concern in many scientific fields. We address here two problems: (1) quantifying the deformation between two given shapes and (2) transporting this deformation to morph a third shape. These operations can be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f035adb263e50c38d261e0e64a739871
https://hal.sorbonne-universite.fr/hal-03249318
https://hal.sorbonne-universite.fr/hal-03249318
Publikováno v:
Journal of Machine Learning Research
Journal of Machine Learning Research, Microtome Publishing, 2021, 22 (74), pp.1-6
HAL
Journal of Machine Learning Research, Microtome Publishing, 2021, 22 (74), pp.1-6
HAL
International audience; The KeOps library provides a fast and memory-efficient GPU support for tensors whose entries are given by a mathematical formula, such as kernel and distance matrices. KeOps alleviates the major bottleneck of tensor-centric li
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::569e5c0194fd238b573777f45e5a36d1
https://hal.archives-ouvertes.fr/hal-02517462v2/file/KeOps_2021.pdf
https://hal.archives-ouvertes.fr/hal-02517462v2/file/KeOps_2021.pdf
Autor:
Martin Bauer, Rudrasis Chakraborty, Benjamin Charlier, Nicolas Charon, Hyo-young Choi, James Damon, Loic Devilliers, Aasa Feragen, Tom Fletcher, Joan Glaunès, Polina Golland, Pietro Gori, Junpyo Hong, Sarang Joshi, Sungkyu Jung, Zhiyuan Liu, Marco Lorenzi, J.S. Marron, Stephen Marsland, Nina Miolane, Jan Modersitzki, Klas Modin, Marc Niethammer, Tom Nye, Beatriz Paniagua, Xavier Pennec, Stephen M. Pizer, Thomas Polzin, Laurent Risser, Pierre Roussillon, Jörn Schulz, Ankur Sharma, Stefan Sommer, Anuj Srivastava, Liyun Tu, Baba C. Vemuri, François-Xavier Vialard, Jared Vicory, Jiyao Wang, William M. Wells, Miaomiao Zhang, Ruiyi Zhang
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::350cb20e460a6fc027889a9e7cafa455
https://doi.org/10.1016/b978-0-12-814725-2.00005-4
https://doi.org/10.1016/b978-0-12-814725-2.00005-4
Publikováno v:
Riemannian Geometric Statistics in Medical Image Analysis
Riemannian Geometric Statistics in Medical Image Analysis, Elsevier, pp.441-477, 2020, ⟨10.1016/B978-0-12-814725-2.00021-2⟩
Riemannian Geometric Statistics in Medical Image Analysis, Elsevier, pp.441-477, 2020, ⟨10.1016/B978-0-12-814725-2.00021-2⟩
This chapter provides an overview of some mathematical and computational models that have been proposed over the past few years for defining data attachment terms on shape spaces of curves or surfaces. In all these models shapes are seen as elements
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d20b36ea250fc4c8f2d9929537410f3
https://hal.telecom-paristech.fr/hal-02341029
https://hal.telecom-paristech.fr/hal-02341029
Autor:
Benjamin Charlier, Alain Trouvé, Karteek Popuri, Sieun Lee, Evgeniy Lebed, Mirza Faisal Beg, Marinko V. Sarunic, Nicolas Charon
Publikováno v:
Medical Image Analysis
Medical Image Analysis, Elsevier, 2017, 35, pp.570-581. ⟨10.1016/j.media.2016.08.012⟩
Medical Image Analysis, Elsevier, 2017, 35, pp.570-581. ⟨10.1016/j.media.2016.08.012⟩
International audience; We propose a novel approach for quantitative shape variability analysis in reti-nal optical coherence tomography images using the functional shape (fshape) framework. The fshape framework uses surface geometry together with fu
Publikováno v:
HAL
We take up on recent work on the Riemannian geometry of generative networks to propose a new approach for learning both a manifold structure and a Riemannian metric from data. It allows the derivation of statistical analysis on manifolds without the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::6ab0a6a20dc21b6db7504729fc8cd4a2
https://hal.inria.fr/hal-01828949/file/nips_2018.pdf
https://hal.inria.fr/hal-01828949/file/nips_2018.pdf