Skip to content

Commit 0a9763c

Browse files
committed
cleanup reference years in readme
1 parent e26e69f commit 0a9763c

File tree

2 files changed

+14
-14
lines changed

2 files changed

+14
-14
lines changed

README.md

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -195,18 +195,18 @@ You can also post bug reports and feature requests in Github issues. Make sure t
195195

196196
[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.
197197

198-
[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.
198+
[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).
199199

200200
[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.
201201

202202
[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816.
203203

204204
[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.
205205

206-
[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.
206+
[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).
207207

208-
[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.
208+
[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.
209209

210-
[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.
210+
[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.
211211

212212
[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) .

docs/source/readme.rst

Lines changed: 10 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -283,10 +283,10 @@ conditional gradient: analysis of convergence and
283283
applications <https://arxiv.org/pdf/1510.06567.pdf>`__. arXiv preprint
284284
arXiv:1510.06567.
285285

286-
[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, `Mapping estimation
287-
for discrete optimal
286+
[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), `Mapping
287+
estimation for discrete optimal
288288
transport <http://remi.flamary.com/biblio/perrot2016mapping.pdf>`__,
289-
Neural Information Processing Systems (NIPS), 2016.
289+
Neural Information Processing Systems (NIPS).
290290

291291
[9] Schmitzer, B. (2016). `Stabilized Sparse Scaling Algorithms for
292292
Entropy Regularized Transport
@@ -303,19 +303,19 @@ arXiv:1607.05816.
303303
Analysis <https://arxiv.org/pdf/1608.08063.pdf>`__. arXiv preprint
304304
arXiv:1608.08063.
305305

306-
[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon,
306+
[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016),
307307
`Gromov-Wasserstein averaging of kernel and distance
308308
matrices <http://proceedings.mlr.press/v48/peyre16.html>`__
309-
International Conference on Machine Learning (ICML). 2016.
309+
International Conference on Machine Learning (ICML).
310310

311-
[13] Mémoli, Facundo. `Gromov–Wasserstein distances and the metric
312-
approach to object
311+
[13] Mémoli, Facundo (2011). `Gromov–Wasserstein distances and the
312+
metric approach to object
313313
matching <https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf>`__.
314-
Foundations of computational mathematics 11.4 (2011): 417-487.
314+
Foundations of computational mathematics 11.4 : 417-487.
315315

316-
[14] Knott, M. and Smith, C. S. `On the optimal mapping of
316+
[14] Knott, M. and Smith, C. S. (1984).`On the optimal mapping of
317317
distributions <https://link.springer.com/article/10.1007/BF00934745>`__,
318-
Journal of Optimization Theory and Applications Vol 43, 1984.
318+
Journal of Optimization Theory and Applications Vol 43.
319319

320320
[15] Peyré, G., & Cuturi, M. (2018). `Computational Optimal
321321
Transport <https://arxiv.org/pdf/1803.00567.pdf>`__ .

0 commit comments

Comments
 (0)