1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y. & Zheng, X. (2016). arXiv: 1603.04467.
2. La théorie générale des couches minces
3. Als-Nielsen, J. & McMorrow, D. (2002). Elements of Modern X-ray Physics, 2nd ed. Chichester: Wiley & Sons.
4. GenX: an extensible X-ray reflectivity refinement program utilizing differential evolution
5. Bottou, L. (1991). Stochastic Gradient Learning in Neural Networks. In Proceedings of Neuro-Nimes. Nanterre: EC2.