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., Mané, 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., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., & Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org. https://www.tensorflow.org/
2. Al-Eidan, R. M., Al-Khalifa, H. S., & Al-Salman, A. S. (2020). Deep-learning-based models for pain recognition: A systematic review. Applied Sciences, 10, 5984.
3. Amir, S., Gandelsman, Y., Bagon, S., & Dekel, T. (2021). Deep ViT features as dense visual descriptors. arXiv preprint arXiv:2112.05814.
4. Amir, S., Zamansky, A., & van der Linden, D. (2017). K9-blyzer-towards video-based automatic analysis of canine behavior. In Proceedings of Animal–Computer Interaction 2017.
5. Anand, K. J., Stevens, B. J., McGrath, P. J., et al. (2007). Pain in neonates and infants: Pain research and clinical management series (Vol. 10). Philedelphia: Elsevier Health Sciences.