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Your search keyword '"Joan Glaunès"' showing total 14 results
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14 results on '"Joan Glaunès"'

9. Kernel Operations on the GPU, with Autodiff,without Memory Overflows

Author

Benjamin Charlier, Jean Feydy, Joan Glaunès, François-David Collin, Ghislain DURIF, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre de Mathématiques et de Leurs Applications (CMLA), École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS), Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), and Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)

Subjects
kernel methods, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], GPU, Gaussian processes, [STAT.CO]Statistics [stat]/Computation [stat.CO], automatic differentiation
Abstract

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 libraries for kernel and geometric applications: memory consumption. It also supports automatic differentiation and outperforms standard GPU baselines, including PyTorch CUDA tensors or the Halide and TVM libraries. KeOps combines optimized C++/CUDA schemes with binders for high-level languages: Python (Numpy and PyTorch), Matlab and GNU R. As a result, high-level "quadratic" codes can now scale up to large data sets with millions of samples processed in seconds. KeOps brings graphics-like performances for kernel methods and is freely available on standard repositories (PyPi, CRAN). To showcase its versatility, we provide tutorials in a wide range of settings online at www.kernel-operations.io.

Published
2021

10. Contributors

Author

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, and Ruiyi Zhang

Published
2020
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11. Joint T1 and brain fiber log-demons registration using currents to model geometry

Author

Viviana, Siless, Joan, Glaunès, Pamela, Guevara, Jean-François, Mangin, Cyril, Poupon, Denis, Le Bihan, Bertrand, Thirion, and Pierre, Fillard

Subjects
Diffusion Tensor Imaging, Subtraction Technique, Image Interpretation, Computer-Assisted, Brain, Humans, Reproducibility of Results, Image Enhancement, Nerve Fibers, Myelinated, Sensitivity and Specificity, Algorithms
Abstract

We present an extension of the diffeomorphic Geometric Demons algorithm which combines the iconic registration with geometric constraints. Our algorithm works in the log-domain space, so that one can efficiently compute the deformation field of the geometry. We represent the shape of objects of interest in the space of currents which is sensitive to both location and geometric structure of objects. Currents provides a distance between geometric structures that can be defined without specifying explicit point-to-point correspondences. We demonstrate this framework by registering simultaneously T1 images and 65 fiber bundles consistently extracted in 12 subjects and compare it against non-linear T1, tensor, and multi-modal T1 + Fractional Anisotropy (FA) registration algorithms. Results show the superiority of the Log-domain Geometric Demons over their purely iconic counterparts.

Published
2013

12. Modelling morphological variability of the hippocampus using manifold learning and large deformations

Author

Claire Cury, Joan Glaunès, Emilie Gerardin, Marie Chupin, Olivier Colliot, Equipe NEMESIS - Centre de Recherches de l'Institut du Cerveau et de la Moelle épinière (NEMESIS-CRICM), Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Cury, Claire, Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [APHP]-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [APHP], Equipe NEMESIS - Centre de Recherches de l'Institut du Cerveau et de la Moelle épinière ( NEMESIS-CRICM ), Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière ( CRICM ), Centre National de la Recherche Scientifique ( CNRS ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ), Algorithms, models and methods for images and signals of the human brain ( ARAMIS ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute ( ICM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -CHU Pitié-Salpêtrière [APHP]-Centre National de la Recherche Scientifique ( CNRS ), Mathématiques Appliquées à Paris 5 ( MAP5 - UMR 8145 ), and Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National des Sciences Mathématiques et de leurs Interactions-Centre National de la Recherche Scientifique ( CNRS )

Subjects
[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging
Abstract

International audience; The hippocampus is a structure of the temporal lobe of the brain which plays an important role in memory processes and in many neuropsychiatric disorders, including Alzheimer's disease, epilepsy and schizophrenia. Here, we present a method to infer the anatomical variability of the hippocampus, based on the LDDMM (Large Deformation Diffeomorphic Metric Mapping) framework and a manifold learning approach to capture the geometry of the population in the space of shapes.

Published
2012

13. Surface matching via currents

Author

Marc, Vaillant and Joan, Glaunès

Subjects
Imaging, Three-Dimensional, Artificial Intelligence, Subtraction Technique, Image Interpretation, Computer-Assisted, Humans, Reproducibility of Results, Computer Simulation, Image Enhancement, Models, Biological, Sensitivity and Specificity, Algorithms, Pattern Recognition, Automated
Abstract

We present a new method for computing an optimal deformation between two arbitrary surfaces embedded in Euclidean 3-dimensional space. Our main contribution is in building a norm on the space of surfaces via representation by currents of geometric measure theory. Currents are an appropriate choice for representations because they inherit natural transformation properties from differential forms. We impose a Hilbert space structure on currents, whose norm gives a convenient and practical way to define a matching functional. Using this Hilbert space norm, we also derive and implement a surface matching algorithm under the large deformation framework, guaranteeing that the optimal solution is a one-to-one regular map of the entire ambient space. We detail an implementation of this algorithm for triangular meshes and present results on 3D face and medical image data.

Published
2007

14. Landmark Matching via Large Deformation Diffeomorphisms on the Sphere.

Author

Joan Glaunès, Marc Vaillant, and Michael I. Miller

Abstract

This paper presents a methodology and algorithm for generating diffeomorphisms of the sphere onto itself, given the displacements of a finite set of template landmarks. Deformation maps are constructed by integration of velocity fields that minimize a quadratic smoothness energy under the specified landmark constraints. We present additional formulations of this problem which incorporate a given error variance in the positions of the landmarks. Finally, some experimental results are presented. This work has application in brain mapping, where surface data is typically mapped to the sphere as a common coordinate system. [ABSTRACT FROM AUTHOR]

Published
2004

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