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  • 最优传输传记(optimal transport)一——最优传输模型

    参考文献:

    [1] G. Monge. Mémoire sur la théorie des déblais et des remblais. Histoire de l’Académie Royale des Sciences de Paris, avec les Mémoires de Mathématique et de Physique pour la même année, pages 666–704, 1781.

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    [7] Cuturi, Marco. "Sinkhorn distances: Lightspeed computation of optimal transport." Advances in neural information processing systems(NIPS). 2013.

    [8] Genevay, A., Cuturi, M., Peyré, G., & Bach, F. (2016). Stochastic optimization for large-scale optimal transport. In Advances in Neural Information Processing Systems (NIPS)(pp. 3440-3448).

    [9] Dvurechensky, P., Gasnikov, A. & Kroshnin, A.. (2018). Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn’s Algorithm. Proceedings of the 35th International Conference on Machine Learning(ICML), in PMLR.80:1367-1376

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    [18] Srivastava, S., Cevher, V., Dinh, Q. & Dunson, D.. (2015). WASP: Scalable Bayes via barycenters of subset posteriors. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics(AISTATS), in PMLR 38:912-920.

    [19] Bonneel, N., Peyré, G., & Cuturi, M. (2016). Wasserstein barycentric coordinates: histogram regression using optimal transport. ACM Trans. Graph., 35(4), 71-1.

    [20]Ye, J., Wu, P., Wang, J. Z., & Li, J. (2017). Fast discrete distribution clustering using Wasserstein barycenter with sparse support. IEEE Transactions on Signal Processing65(9), 2317-2332.

    [21] Staib, M. Claici, S., Solomon, J. M., and Jegelka, S. Parallel streaming Wasserstein barycenters. In Advances in Neural Information Processing Systems(NIPS), pp. 2644–2655, 2017.

    [22] Anderes, E. Borgwardt, S., and Miller, J. Discrete Wasserstein barycenters: Optimal transport for discrete data. Math Meth Oper Res, 84(2):389–409, October 2016. ISSN 1432-2994, 1432-5217. doi: 10.1007/s00186-016-0549-x.

    [23] Claici, S., Chien, E., & Solomon, J. (2018). Stochastic Wasserstein Barycenters. ICML.

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    [26] M. Cuturi and A. Doucet. Fast computation of Wasserstein barycenters. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 685–693, 2014.

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  • 原文地址:https://www.cnblogs.com/XiangGu/p/9190711.html
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