1. Blondel, M., A. Mensch, and J. P. Vert, 2021, Differentiable divergences between time series: Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research, vol. 130, 3853–3861, https://proceedings.mlr.press/v130/blondel21a.html.
2. Cycle-skipping mitigation using misfit measurements based on differentiable dynamic time warping
3. Cuturi, M., and M. Blondel, 2017, Soft-DTW: a differentiable loss function for time-series: Proceedings of the 34th International Conference on Machine Learning, vol. 70 (ICML'17), 894–903, https://dl.acm.org/doi/10.5555/3305381.3305474.
4. An overview of full-waveform inversion in exploration geophysics
5. Giulia, L., A. Rudi, M. Pontil, and C. Ciliberto, 2018, Differential properties of sinkhorn approximation for learning with Wasserstein distance: International Conference of Neural Information Processing Systems, 5864–5874.