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.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). http://tensorflow.org/ , software available from tensorflow.org
2. Barrett, F.J.: Coda–creativity and improvisation in jazz and organizations: implications for organizational learning. Organ. Sci. 9(5), 605–622 (1998)
3. Bastien, D.T., Hostager, T.J.: Jazz as a process of organizational innovation. Commun. Res. 15(5), 582–602 (1988)
4. Berliner, P.: Thinking in jazz: composing in the moment. Jazz Educ. J. 26, 241 (1994)
5. Biles, J.A.: Genjam in transition: from genetic jammer to generative jammer. In: Generative Art, vol. 2002 (2002)