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. J., Harp,
A., Irving, G., Isard, M., Jia, Y., Józefowicz, R., Kaiser, L., Kudlur,
M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D. G., Olah,
C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker,
P. A., Vanhoucke, V., Vasudevan, V., Viégas, F. B., Vinyals, O.,
Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow:
Large-Scale Machine Learning on Heterogeneous Systems, tensorflow.org [code],
https://www.tensorflow.org/ (last access: 12 December 2021), 2015. a
2. Alahmari, S. S., Goldgof, D. B., Mouton, P. R., and Hall, L. O.: Challenges
for the Repeatability of Deep Learning Models, IEEE Access, 8,
211860–211868, https://doi.org/10.1109/ACCESS.2020.3039833, 2020. a, b, c, d
3. Allaire, J. J., Ushey, K., Tang, Y., and Eddelbuettel, D.: Reticulate: R
Interface to Python, GitHub [code], https://github.com/rstudio/reticulate (last access: 12 December 2021),
2017. a
4. Association for Computing Machinery (ACM): Artifact Review and Badging
Version 2.0, ACM,
https://www.acm.org/publications/policies/artifact-review-badging,
2021. a
5. Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l