Houy, C., Fettke, P., Loos, P., van der Aalst, W. M. P., Krogstie, J., 2010. BPM-in- the-large - towards a higher level of abstraction in business process management. In: EGES/GISP. Vol. 334 of IFIP Advances in Information and Communication Technology. Springer, pp. 233–244. Karpathy, A., 2016. The unreasonable effectiveness of recurrent neural networks. Accessed 6 Dec, 2016. Karpathy, A., Johnson, J., Li, F., 2015. Visualizing and understanding recurrent networks. CoRR abs/1506.02078. Lakshmanan, G. T., Shamsi, D., Doganata, Y. N., Unuvar, M., Khalaf, R., 2015. A Markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42 (1), 97–126. Le, M., Gabrys, B., Nauck, D., 2012. A hybrid model for business process event prediction. In: SGAI Conf. Springer, pp. 179–192. Li, J., Chen, X., Hovy, E. H., Jurafsky, D., 2015. Visualizing and understanding neural models in NLP. CoRR abs/1506.01066. Lin, H. W., Tegmark, M., Jun. 2016. Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language. CoRR abs/1606.06737. Maaten, L. v. d., Hinton, G., 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9 (Nov), 2579–2605. Mikolov, T., Kombrink, S., Burget, L., ˇ Cernock` y, J., Khudanpur, S., 2011. Ex- tensions of recurrent neural network language model. In: Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on. pp. 5528– 5531. Schmidhuber, J., 2015. Deep learning in neural networks: An overview. Neural Networks 61, 85–117. Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15 (1), 1929–1958. Strobelt, H., Gehrmann, S., Huber, B., Pfister, H., Rush, A. M., 2016. Visual analy- sis of hidden state dynamics in recurrent neural networks. CoRR abs/1606.07461. Sutskever, I., Martens, J., Hinton, G. E., 2011. Generating text with recurrent neural networks. In: ICML. Omnipress, pp. 1017–1024. 33 Huang, A., Wu, R., 2016. Deep learning for music. CoRR abs/1606.04930. LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521, 436–444.