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author | Rémi Flamary <remi.flamary@gmail.com> | 2018-05-09 13:08:53 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2018-05-09 13:08:53 +0200 |
commit | 0a9763ce0e83106daa322566398218aa4a297fe1 (patch) | |
tree | f7920f6a9ed38e37710777ba919d09760295c073 | |
parent | e26e69f11498a85148f0df9776c7fb0fca4545f1 (diff) |
cleanup reference years in readme
-rw-r--r-- | README.md | 8 | ||||
-rw-r--r-- | docs/source/readme.rst | 20 |
2 files changed, 14 insertions, 14 deletions
@@ -195,7 +195,7 @@ You can also post bug reports and feature requests in Github issues. Make sure t [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https://arxiv.org/pdf/1510.06567.pdf). arXiv preprint arXiv:1510.06567. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS), 2016. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS). [9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519. @@ -203,10 +203,10 @@ You can also post bug reports and feature requests in Github issues. Make sure t [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. -[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). -[13] Mémoli, Facundo. [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 (2011): 417-487. +[13] Mémoli, Facundo (2011). [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 : 417-487. -[14] Knott, M. and Smith, C. S. [On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43, 1984. +[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43. [15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) . diff --git a/docs/source/readme.rst b/docs/source/readme.rst index d73d293..725c207 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -283,10 +283,10 @@ conditional gradient: analysis of convergence and applications <https://arxiv.org/pdf/1510.06567.pdf>`__. arXiv preprint arXiv:1510.06567. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, `Mapping estimation -for discrete optimal +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), `Mapping +estimation for discrete optimal transport <http://remi.flamary.com/biblio/perrot2016mapping.pdf>`__, -Neural Information Processing Systems (NIPS), 2016. +Neural Information Processing Systems (NIPS). [9] Schmitzer, B. (2016). `Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport @@ -303,19 +303,19 @@ arXiv:1607.05816. Analysis <https://arxiv.org/pdf/1608.08063.pdf>`__. arXiv preprint arXiv:1608.08063. -[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), `Gromov-Wasserstein averaging of kernel and distance matrices <http://proceedings.mlr.press/v48/peyre16.html>`__ -International Conference on Machine Learning (ICML). 2016. +International Conference on Machine Learning (ICML). -[13] Mémoli, Facundo. `Gromov–Wasserstein distances and the metric -approach to object +[13] Mémoli, Facundo (2011). `Gromov–Wasserstein distances and the +metric approach to object matching <https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf>`__. -Foundations of computational mathematics 11.4 (2011): 417-487. +Foundations of computational mathematics 11.4 : 417-487. -[14] Knott, M. and Smith, C. S. `On the optimal mapping of +[14] Knott, M. and Smith, C. S. (1984).`On the optimal mapping of distributions <https://link.springer.com/article/10.1007/BF00934745>`__, -Journal of Optimization Theory and Applications Vol 43, 1984. +Journal of Optimization Theory and Applications Vol 43. [15] Peyré, G., & Cuturi, M. (2018). `Computational Optimal Transport <https://arxiv.org/pdf/1803.00567.pdf>`__ . |