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Nicolas Courty | Nicolas Bonneel | Julie Digne | Julien Lacombe | N. Courty | Nicolas Bonneel | Julie Digne | Julien Lacombe
[1] Evgeny Burnaev,et al. Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization , 2021, ICLR.
[2] Justin Solomon,et al. Continuous Regularized Wasserstein Barycenters , 2020, NeurIPS.
[3] A. Cloninger,et al. Linear Optimal Transport Embedding: Provable fast Wasserstein distance computation and classification for nonlinear problems , 2020, ArXiv.
[4] Yongxin Chen,et al. Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks , 2020, ICML.
[5] Marco Cuturi,et al. Debiased Sinkhorn barycenters , 2020, ICML.
[6] S. Koukoulas,et al. Clustering measure-valued data with Wasserstein barycenters , 2019 .
[7] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[8] Marco Cuturi,et al. Ground Metric Learning on Graphs , 2019, Journal of Mathematical Imaging and Vision.
[9] A. Trouvé,et al. Fast and Scalable Optimal Transport for Brain Tractograms , 2019, MICCAI.
[10] Quentin Mérigot,et al. Quantitative stability of optimal transport maps and linearization of the 2-Wasserstein space , 2019, AISTATS.
[11] François-Xavier Vialard,et al. Nonlinear model reduction on metric spaces. Application to one-dimensional conservative PDEs in Wasserstein spaces , 2019, ESAIM: Mathematical Modelling and Numerical Analysis.
[12] Justin Solomon,et al. Learning Embeddings into Entropic Wasserstein Spaces , 2019, ICLR.
[13] Cícero Nogueira dos Santos,et al. Wasserstein Barycenter Model Ensembling , 2019, ICLR.
[14] Gaël Guennebaud,et al. Instant transport maps on 2D grids , 2018, ACM Trans. Graph..
[15] Alain Trouvé,et al. Interpolating between Optimal Transport and MMD using Sinkhorn Divergences , 2018, AISTATS.
[16] Leland McInnes,et al. UMAP: Uniform Manifold Approximation and Projection , 2018, J. Open Source Softw..
[17] Yalin Wang,et al. Variational Wasserstein Clustering , 2018, ECCV.
[18] Antoine Liutkus,et al. Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions , 2018, ICML.
[19] Felipe A. Tobar,et al. Bayesian Learning with Wasserstein Barycenters , 2018, ESAIM: Probability and Statistics.
[20] Steve Oudot,et al. Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport , 2018, NeurIPS.
[21] Ting-Chun Wang,et al. Image Inpainting for Irregular Holes Using Partial Convolutions , 2018, ECCV.
[22] Gabriel Peyré,et al. Computational Optimal Transport , 2018, Found. Trends Mach. Learn..
[23] Sebastian Claici,et al. Stochastic Wasserstein Barycenters , 2018, ICML.
[24] Leland McInnes,et al. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.
[25] Andrea Vedaldi,et al. Deep Image Prior , 2017, International Journal of Computer Vision.
[26] Nicolas Courty,et al. Learning Wasserstein Embeddings , 2017, ICLR.
[27] Jean-Luc Starck,et al. Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning , 2017, SIAM J. Imaging Sci..
[28] Jian Yang,et al. Image Super-Resolution via Deep Recursive Residual Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Gabriel Peyré,et al. Learning Generative Models with Sinkhorn Divergences , 2017, AISTATS.
[30] Narendra Ahuja,et al. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Jérémie Bigot,et al. Geodesic PCA in the Wasserstein space by Convex PCA , 2017 .
[32] Léon Bottou,et al. Wasserstein GAN , 2017, ArXiv.
[33] Stamatios Lefkimmiatis,et al. Non-local Color Image Denoising with Convolutional Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Bernhard Schmitzer,et al. Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems , 2016, SIAM J. Sci. Comput..
[35] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] J. Z. Kolter,et al. Input Convex Neural Networks , 2016, ICML.
[37] Frank Hutter,et al. SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.
[38] Andrea Vedaldi,et al. Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.
[39] Minh N. Do,et al. Semantic Image Inpainting with Deep Generative Models , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Gabriel Peyré,et al. Wasserstein barycentric coordinates , 2016, ACM Trans. Graph..
[41] Marco Cuturi,et al. Fast Dictionary Learning with a Smoothed Wasserstein Loss , 2016, AISTATS.
[42] Gabriel Peyré,et al. Convolutional wasserstein distances , 2015, ACM Trans. Graph..
[43] Marco Cuturi,et al. Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric , 2015, NIPS.
[44] Hossein Mobahi,et al. Learning with a Wasserstein Loss , 2015, NIPS.
[45] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[46] Volkan Cevher,et al. WASP: Scalable Bayes via barycenters of subset posteriors , 2015, AISTATS.
[47] Gabriel Peyré,et al. Iterative Bregman Projections for Regularized Transportation Problems , 2014, SIAM J. Sci. Comput..
[48] Nicolas Courty,et al. Domain Adaptation with Regularized Optimal Transport , 2014, ECML/PKDD.
[49] G. Peyré,et al. Sliced and Radon Wasserstein Barycenters of Measures , 2014, Journal of Mathematical Imaging and Vision.
[50] Arnaud Doucet,et al. Fast Computation of Wasserstein Barycenters , 2013, ICML.
[51] Marco Cuturi,et al. Sinkhorn Distances: Lightspeed Computation of Optimal Transport , 2013, NIPS.
[52] Enhong Chen,et al. Image Denoising and Inpainting with Deep Neural Networks , 2012, NIPS.
[53] Stefan Harmeling,et al. Image denoising: Can plain neural networks compete with BM3D? , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[54] Wolfgang Heidrich,et al. Displacement interpolation using Lagrangian mass transport , 2011, ACM Trans. Graph..
[55] Julien Rabin,et al. Removing Artefacts From Color and Contrast Modifications , 2011, IEEE Transactions on Image Processing.
[56] Julien Rabin,et al. Wasserstein Barycenter and Its Application to Texture Mixing , 2011, SSVM.
[57] Erik Reinhard,et al. Colour Spaces for Colour Transfer , 2011, CCIW.
[58] Alexandr Andoni,et al. Earth mover distance over high-dimensional spaces , 2008, SODA '08.
[59] John D. Hunter,et al. Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.
[60] Marcello Restelli,et al. Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters , 2019, NeurIPS.
[61] Alexandr Andoni,et al. Impossibility of Sketching of the 3D Transportation Metric with Quadratic Cost , 2016, ICALP.
[62] Gustavo K. Rohde,et al. A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images , 2012, International Journal of Computer Vision.
[63] Sameer A. Nene,et al. Columbia Object Image Library (COIL100) , 1996 .