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2020 – today
- 2024
[c78]Othmane Sebbouh, Marco Cuturi, Gabriel Peyré:
Structured Transforms Across Spaces with Cost-Regularized Optimal Transport. AISTATS 2024: 586-594- [c77]Zhenzhang Ye, Gabriel Peyré, Daniel Cremers, Pierre Ablin:
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization. AISTATS 2024: 955-963 - [c76]Francisco Andrade, Gabriel Peyré, Clarice Poon:
Sparsistency for inverse optimal transport. ICLR 2024 - [c75]Valérie Castin, Pierre Ablin, Gabriel Peyré:
How Smooth Is Attention? ICML 2024 - [c74]Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré:
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows. ICML 2024 - [c73]Michael Eli Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré:
How do Transformers Perform In-Context Autoregressive Learning ? ICML 2024 - [i61]Michael E. Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré:
How do Transformers perform In-Context Autoregressive Learning? CoRR abs/2402.05787 (2024) - [i60]Zhenzhang Ye, Gabriel Peyré, Daniel Cremers, Pierre Ablin:
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization. CoRR abs/2402.16748 (2024) - [i59]Raphaël Barboni, Gabriel Peyré, François-Xavier Vialard:
Understanding the training of infinitely deep and wide ResNets with Conditional Optimal Transport. CoRR abs/2403.12887 (2024) - [i58]Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré:
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows. CoRR abs/2405.12888 (2024) - 2023
- [j63]Clarice Poon, Nicolas Keriven, Gabriel Peyré:
The Geometry of Off-the-Grid Compressed Sensing. Found. Comput. Math. 23(1): 241-327 (2023) - [j62]Clarice Poon, Gabriel Peyré:
Smooth over-parameterized solvers for non-smooth structured optimization. Math. Program. 201(1): 897-952 (2023) - [c72]Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel:
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. ICML 2023: 29919-29936 - [c71]Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré:
Abide by the law and follow the flow: conservation laws for gradient flows. NeurIPS 2023 - [i57]Michael E. Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel:
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. CoRR abs/2302.01425 (2023) - [i56]Zaccharie Ramzi, Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Test like you Train in Implicit Deep Learning. CoRR abs/2305.15042 (2023) - [i55]Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré:
Abide by the Law and Follow the Flow: Conservation Laws for Gradient Flows. CoRR abs/2307.00144 (2023) - [i54]Othmane Sebbouh, Marco Cuturi, Gabriel Peyré:
Structured Transforms Across Spaces with Cost-Regularized Optimal Transport. CoRR abs/2311.05788 (2023) - [i53]Valérie Castin, Pierre Ablin, Gabriel Peyré:
Understanding the Regularity of Self-Attention with Optimal Transport. CoRR abs/2312.14820 (2023) - 2022
- [j61]Geert-Jan Huizing
, Gabriel Peyré, Laura Cantini:
Optimal transport improves cell-cell similarity inference in single-cell omics data. Bioinform. 38(8): 2169-2177 (2022) - [c70]Othmane Sebbouh, Marco Cuturi, Gabriel Peyré:
Randomized Stochastic Gradient Descent Ascent. AISTATS 2022: 2941-2969 - [c69]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Sinkformers: Transformers with Doubly Stochastic Attention. AISTATS 2022: 3515-3530 - [c68]Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré:
Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe. AISTATS 2022: 4995-5021 - [c67]Pierre Ablin, Gabriel Peyré:
Fast and accurate optimization on the orthogonal manifold without retraction. AISTATS 2022: 5636-5657 - [c66]Geert-Jan Huizing, Laura Cantini, Gabriel Peyré:
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors. ICML 2022: 9429-9443 - [c65]Meyer Scetbon, Gabriel Peyré, Marco Cuturi:
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs. ICML 2022: 19347-19365 - [c64]Raphaël Barboni, Gabriel Peyré, François-Xavier Vialard:
On global convergence of ResNets: From finite to infinite width using linear parameterization. NeurIPS 2022 - [c63]Michael E. Sander, Pierre Ablin, Gabriel Peyré:
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? NeurIPS 2022 - [i52]Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré:
Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe. CoRR abs/2201.00730 (2022) - [i51]Clarice Poon, Gabriel Peyré:
Smooth over-parameterized solvers for non-smooth structured optimization. CoRR abs/2205.01385 (2022) - [i50]Michael E. Sander, Pierre Ablin, Gabriel Peyré:
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? CoRR abs/2205.14612 (2022) - [i49]Thibault Séjourné, Gabriel Peyré, François-Xavier Vialard:
Unbalanced Optimal Transport, from Theory to Numerics. CoRR abs/2211.08775 (2022) - 2021
- [j60]Matthieu Heitz, Nicolas Bonneel, David Coeurjolly, Marco Cuturi, Gabriel Peyré:
Ground Metric Learning on Graphs. J. Math. Imaging Vis. 63(1): 89-107 (2021) - [c62]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Momentum Residual Neural Networks. ICML 2021: 9276-9287 - [c61]Meyer Scetbon, Marco Cuturi, Gabriel Peyré:
Low-Rank Sinkhorn Factorization. ICML 2021: 9344-9354 - [c60]Clarice Poon, Gabriel Peyré:
Smooth Bilevel Programming for Sparse Regularization. NeurIPS 2021: 1543-1555 - [c59]Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré:
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation. NeurIPS 2021: 8766-8779 - [i48]Geert-Jan Huizing, Laura Cantini, Gabriel Peyré:
Unsupervised Ground Metric Learning using Wasserstein Eigenvectors. CoRR abs/2102.06278 (2021) - [i47]Pierre Ablin, Gabriel Peyré:
Fast and accurate optimization on the orthogonal manifold without retraction. CoRR abs/2102.07432 (2021) - [i46]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Momentum Residual Neural Networks. CoRR abs/2102.07870 (2021) - [i45]Meyer Scetbon, Marco Cuturi, Gabriel Peyré:
Low-Rank Sinkhorn Factorization. CoRR abs/2103.04737 (2021) - [i44]Meyer Scetbon, Gabriel Peyré, Marco Cuturi:
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs. CoRR abs/2106.01128 (2021) - [i43]Clarice Poon, Gabriel Peyré:
Smooth Bilevel Programming for Sparse Regularization. CoRR abs/2106.01429 (2021) - [i42]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Sinkformers: Transformers with Doubly Stochastic Attention. CoRR abs/2110.11773 (2021) - [i41]Othmane Sebbouh, Marco Cuturi, Gabriel Peyré:
Randomized Stochastic Gradient Descent Ascent. CoRR abs/2111.13162 (2021) - [i40]Raphaël Barboni, Gabriel Peyré, François-Xavier Vialard:
Global convergence of ResNets: From finite to infinite width using linear parameterization. CoRR abs/2112.05531 (2021) - 2020
- [c58]Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, Marcelo Pereyra:
Wasserstein Control of Mirror Langevin Monte Carlo. COLT 2020: 3814-3841 - [c57]Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Super-efficiency of automatic differentiation for functions defined as a minimum. ICML 2020: 32-41 - [c56]Lénaïc Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, Gabriel Peyré:
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence. NeurIPS 2020 - [c55]Hicham Janati, Boris Muzellec, Gabriel Peyré, Marco Cuturi:
Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form. NeurIPS 2020 - [c54]Arthur Mensch, Gabriel Peyré:
Online Sinkhorn: Optimal Transport distances from sample streams. NeurIPS 2020 - [i39]Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Super-efficiency of automatic differentiation for functions defined as a minimum. CoRR abs/2002.03722 (2020) - [i38]Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, Marcelo Pereyra:
Wasserstein Control of Mirror Langevin Monte Carlo. CoRR abs/2002.04363 (2020) - [i37]Gwendoline de Bie, Herilalaina Rakotoarison, Gabriel Peyré, Michèle Sebag:
Distribution-Based Invariant Deep Networks for Learning Meta-Features. CoRR abs/2006.13708 (2020)
2010 – 2019
- 2019
- [j59]Gabriel Peyré, Marco Cuturi:
Computational Optimal Transport. Found. Trends Mach. Learn. 11(5-6): 355-607 (2019) - [j58]Jean-François Aujol, Jalal Fadili, Michael Hintermüller, Gabriel Peyré, Gerlind Plonka-Hoch, Gabriele Steidl:
Guest Editorial JMIV Special Issue Mathematics and Image Analysis (MIA). J. Math. Imaging Vis. 61(5): 643-644 (2019) - [j57]Paul Catala, Vincent Duval, Gabriel Peyré:
A Low-Rank Approach to Off-the-Grid Sparse Superresolution. SIAM J. Imaging Sci. 12(3): 1464-1500 (2019) - [j56]Clarice Poon, Gabriel Peyré:
MultiDimensional Sparse Super-Resolution. SIAM J. Math. Anal. 51(1): 1-44 (2019) - [c53]Jalal Fadili, Guillaume Garrigos, Jérôme Malick, Gabriel Peyré:
Model Consistency for Learning with Mirror-Stratifiable Regularizers. AISTATS 2019: 1236-1244 - [c52]Clarice Poon, Nicolas Keriven, Gabriel Peyré:
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization. AISTATS 2019: 1341-1350 - [c51]Aude Genevay, Lénaïc Chizat, Francis R. Bach, Marco Cuturi, Gabriel Peyré:
Sample Complexity of Sinkhorn Divergences. AISTATS 2019: 1574-1583 - [c50]Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouvé, Gabriel Peyré:
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences. AISTATS 2019: 2681-2690 - [c49]Gwendoline de Bie, Gabriel Peyré, Marco Cuturi:
Stochastic Deep Networks. ICML 2019: 1556-1565 - [c48]Arthur Mensch, Mathieu Blondel, Gabriel Peyré:
Geometric Losses for Distributional Learning. ICML 2019: 4516-4525 - [c47]Nicolas Keriven, Gabriel Peyré:
Universal Invariant and Equivariant Graph Neural Networks. NeurIPS 2019: 7090-7099 - [i36]Nicolas Keriven, Gabriel Peyré:
Universal Invariant and Equivariant Graph Neural Networks. CoRR abs/1905.04943 (2019) - [i35]Arthur Mensch, Mathieu Blondel, Gabriel Peyré:
Geometric Losses for Distributional Learning. CoRR abs/1905.06005 (2019) - [i34]Thibault Séjourné, Jean Feydy, François-Xavier Vialard, Alain Trouvé, Gabriel Peyré:
Sinkhorn Divergences for Unbalanced Optimal Transport. CoRR abs/1910.12958 (2019) - [i33]Matthieu Heitz, Nicolas Bonneel, David Coeurjolly, Marco Cuturi, Gabriel Peyré:
Ground Metric Learning on Graphs. CoRR abs/1911.03117 (2019) - [i32]Clarice Poon, Gabriel Peyré:
Degrees of freedom for off-the-grid sparse estimation. CoRR abs/1911.03577 (2019) - 2018
- [j55]Lenaïc Chizat, Gabriel Peyré, Bernhard Schmitzer, François-Xavier Vialard:
An Interpolating Distance Between Optimal Transport and Fisher-Rao Metrics. Found. Comput. Math. 18(1): 1-44 (2018) - [j54]Lenaïc Chizat, Gabriel Peyré, Bernhard Schmitzer, François-Xavier Vialard:
Scaling algorithms for unbalanced optimal transport problems. Math. Comput. 87(314): 2563-2609 (2018) - [j53]Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré:
Bayesian Modeling of Motion Perception Using Dynamical Stochastic Textures. Neural Comput. 30(12) (2018) - [j52]Morgan A. Schmitz, Matthieu Heitz, Nicolas Bonneel, Fred Maurice Ngolè Mboula, David Coeurjolly, Marco Cuturi, Gabriel Peyré, Jean-Luc Starck:
Wasserstein Dictionary Learning: Optimal Transport-Based Unsupervised Nonlinear Dictionary Learning. SIAM J. Imaging Sci. 11(1): 643-678 (2018) - [j51]Jalal Fadili, Jérôme Malick, Gabriel Peyré:
Sensitivity Analysis for Mirror-Stratifiable Convex Functions. SIAM J. Optim. 28(4): 2975-3000 (2018) - [j50]Marco Cuturi, Gabriel Peyré:
Semidual Regularized Optimal Transport. SIAM Rev. 60(4): 941-965 (2018) - [j49]Samuel Vaiter, Gabriel Peyré, Jalal Fadili:
Model Consistency of Partly Smooth Regularizers. IEEE Trans. Inf. Theory 64(3): 1725-1737 (2018) - [c46]Aude Genevay, Gabriel Peyré, Marco Cuturi:
Learning Generative Models with Sinkhorn Divergences. AISTATS 2018: 1608-1617 - [i31]Clarice Poon, Nicolas Keriven, Gabriel Peyré:
A Dual Certificates Analysis of Compressive Off-the-Grid Recovery. CoRR abs/1802.08464 (2018) - [i30]Clarice Poon, Nicolas Keriven, Gabriel Peyré:
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization. CoRR abs/1810.03340 (2018) - [i29]Marco Cuturi, Gabriel Peyré:
Semi-dual Regularized Optimal Transport. CoRR abs/1811.05527 (2018) - [i28]Gwendoline de Bie, Gabriel Peyré, Marco Cuturi:
Stochastic Deep Networks. CoRR abs/1811.07429 (2018) - 2017
- [j48]Jalal Fadili, Gitta Kutyniok, Gabriel Peyré, Gerlind Plonka-Hoch, Gabriele Steidl:
JMIV Special Issue Mathematics and Image Analysis. J. Math. Imaging Vis. 59(3): 371-372 (2017) - [j47]Jingwei Liang, Jalal Fadili, Gabriel Peyré:
Local Convergence Properties of Douglas-Rachford and Alternating Direction Method of Multipliers. J. Optim. Theory Appl. 172(3): 874-913 (2017) - [j46]Jingwei Liang, Jalal Fadili, Gabriel Peyré:
Activity Identification and Local Linear Convergence of Forward-Backward-type Methods. SIAM J. Optim. 27(1): 408-437 (2017) - [j45]Guillaume Carlier, Vincent Duval, Gabriel Peyré, Bernhard Schmitzer:
Convergence of Entropic Schemes for Optimal Transport and Gradient Flows. SIAM J. Math. Anal. 49(2): 1385-1418 (2017) - [c45]Jean Feydy, Benjamin Charlier, François-Xavier Vialard, Gabriel Peyré:
Optimal Transport for Diffeomorphic Registration. MICCAI (1) 2017: 291-299 - [i27]Jalal Fadili, Jérôme Malick, Gabriel Peyré:
Sensitivity Analysis for Mirror-Stratifiable Convex Functions. CoRR abs/1707.03194 (2017) - [i26]Morgan A. Schmitz, Matthieu Heitz, Nicolas Bonneel, Fred Maurice Ngolè Mboula, David Coeurjolly, Marco Cuturi, Gabriel Peyré, Jean-Luc Starck:
Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning. CoRR abs/1708.01955 (2017) - [i25]Paul Catala, Vincent Duval, Gabriel Peyré:
A Low-Rank Approach to Off-The-Grid Sparse Deconvolution. CoRR abs/1712.08800 (2017) - 2016
- [j44]Jingwei Liang, Jalal Fadili, Gabriel Peyré:
Convergence rates with inexact non-expansive operators. Math. Program. 159(1-2): 403-434 (2016) - [j43]Giacomo Nardi, Gabriel Peyré, François-Xavier Vialard:
Geodesics on Shape Spaces with Bounded Variation and Sobolev Metrics. SIAM J. Imaging Sci. 9(1): 238-274 (2016) - [j42]Marco Cuturi, Gabriel Peyré:
A Smoothed Dual Approach for Variational Wasserstein Problems. SIAM J. Imaging Sci. 9(1): 320-343 (2016) - [j41]Guillaume Tartavel, Gabriel Peyré, Yann Gousseau:
Wasserstein Loss for Image Synthesis and Restoration. SIAM J. Imaging Sci. 9(4): 1726-1755 (2016) - [j40]Nicolas Bonneel, Gabriel Peyré, Marco Cuturi:
Wasserstein barycentric coordinates: histogram regression using optimal transport. ACM Trans. Graph. 35(4): 71:1-71:10 (2016) - [j39]Justin Solomon, Gabriel Peyré, Vladimir G. Kim, Suvrit Sra:
Entropic metric alignment for correspondence problems. ACM Trans. Graph. 35(4): 72:1-72:13 (2016) - [c44]Antoine Rolet, Marco Cuturi, Gabriel Peyré:
Fast Dictionary Learning with a Smoothed Wasserstein Loss. AISTATS 2016: 630-638 - [c43]Gabriel Peyré, Marco Cuturi, Justin Solomon:
Gromov-Wasserstein Averaging of Kernel and Distance Matrices. ICML 2016: 2664-2672 - [c42]Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis R. Bach:
Stochastic Optimization for Large-scale Optimal Transport. NIPS 2016: 3432-3440 - [c41]Jingwei Liang, Jalal Fadili, Gabriel Peyré:
A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization. NIPS 2016: 4035-4043 - [c40]Kévin Degraux, Gabriel Peyré, Jalal Fadili, Laurent Jacques:
Sparse Support Recovery with Non-smooth Loss Functions. NIPS 2016: 4269-4277 - [i24]Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis R. Bach:
Stochastic Optimization for Large-scale Optimal Transport. CoRR abs/1605.08527 (2016) - [i23]Vinayak Abrol, Olivier Absil, Pierre-Antoine Absil, Sandrine Anthoine, Philippe Antoine, Thomas Arildsen, Nancy Bertin, Folkert Bleichrodt, Jérôme Bobin, Anne Bol, Antoine Bonnefoy, Francesco Caltagirone, Valerio Cambareri, Cecile Chenot, Vladimir S. Crnojevic, Marie Danková, Kévin Degraux, Jens Eisert, Mohamed-Jalal Fadili, Marylou Gabrié, Nicolas Gac, Daniele Giacobello, Carlos A. Gomez Gonzalez, Adriana Gonzalez, Pierre-Yves Gousenbourger, Mads Græsbøll Christensen, Rémi Gribonval, Stéphanie Guérit, Shaoguang Huang, Paul Irofti, Laurent Jacques, Ulugbek S. Kamilov, Srdan Kitic, Martin Kliesch, Florent Krzakala, John A. Lee, Wenzhi Liao, Tobias Lindstrøm Jensen, Andre Manoel, Hassan Mansour, Ali Mohammad-Djafari, Amirafshar Moshtaghpour, Fred Maurice Ngolè Mboula, Benoît Pairet, Marko Panic, Gabriel Peyré, Aleksandra Pizurica, Pavel Rajmic, Matthieu Roblin, Ingo Roth, Anil Kumar Sao, Pulkit Sharma, Jean-Luc Starck, Eric W. Tramel, Toon van Waterschoot, Dejan Vukobratovic, Li Wang, Benedikt Wirth, Gerhard Wunder, Hongyan Zhang:
Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16). CoRR abs/1609.04167 (2016) - [i22]Kévin Degraux, Gabriel Peyré, Jalal Fadili, Laurent Jacques:
Sparse Support Recovery with Non-smooth Loss Functions. CoRR abs/1611.01030 (2016) - [i21]Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré:
Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures. CoRR abs/1611.01390 (2016) - [i20]Gabriel Peyré, Lenaïc Chizat, François-Xavier Vialard, Justin Solomon:
Quantum Optimal Transport for Tensor Field Processing. CoRR abs/1612.08731 (2016) - 2015
- [j38]Vincent Duval, Gabriel Peyré:
Exact Support Recovery for Sparse Spikes Deconvolution. Found. Comput. Math. 15(5): 1315-1355 (2015) - [j37]Nicolas Bonneel, Julien Rabin, Gabriel Peyré, Hanspeter Pfister:
Sliced and Radon Wasserstein Barycenters of Measures. J. Math. Imaging Vis. 51(1): 22-45 (2015) - [j36]Guillaume Tartavel, Yann Gousseau, Gabriel Peyré:
Variational Texture Synthesis with Sparsity and Spectrum Constraints. J. Math. Imaging Vis. 52(1): 124-144 (2015) - [j35]Jalal Fadili, Gitta Kutyniok, Gabriel Peyré, Gerlind Plonka-Hoch, Gabriele Steidl:
Guest Editorial: Mathematics and Image Analysis. J. Math. Imaging Vis. 52(3): 315-316 (2015) - [j34]Gabriel Peyré:
Entropic Approximation of Wasserstein Gradient Flows. SIAM J. Imaging Sci. 8(4): 2323-2351 (2015) - [j33]Jean-David Benamou, Guillaume Carlier, Marco Cuturi, Luca Nenna, Gabriel Peyré:
Iterative Bregman Projections for Regularized Transportation Problems. SIAM J. Sci. Comput. 37(2) (2015) - [j32]Justin Solomon, Fernando de Goes, Gabriel Peyré, Marco Cuturi, Adrian Butscher, Andy Nguyen, Tao Du, Leonidas J. Guibas:
Convolutional wasserstein distances: efficient optimal transportation on geometric domains. ACM Trans. Graph. 34(4): 66:1-66:11 (2015) - [c39]Vincent Duval, Gabriel Peyré:
The non degenerate source condition: Support robustness for discrete and continuous sparse deconvolution. CAMSAP 2015: 49-52 - [c38]Alexandre Gramfort, Gabriel Peyré, Marco Cuturi:
Fast Optimal Transport Averaging of Neuroimaging Data. IPMI 2015: 261-272 - [c37]Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré:
Biologically Inspired Dynamic Textures for Probing Motion Perception. NIPS 2015: 1918-1926 - [c36]Jingwei Liang, Jalal Fadili, Gabriel Peyré, D. Russell Luke:
Activity Identification and Local Linear Convergence of Douglas-Rachford/ADMM under Partial Smoothness. SSVM 2015: 642-653 - [i19]Vincent Duval, Gabriel Peyré:
Sparse Spikes Deconvolution on Thin Grids. CoRR abs/1503.08577 (2015) - [i18]Alexandre Gramfort, Gabriel Peyré, Marco Cuturi:
Fast Optimal Transport Averaging of Neuroimaging Data. CoRR abs/1503.08596 (2015) - [i17]Quentin Denoyelle, Vincent Duval, Gabriel Peyré:
Support Recovery for Sparse Deconvolution of Positive Measures. CoRR abs/1506.08264 (2015) - [i16]