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. 

Huang, A., Wu, R., 2016. Deep learning for music. CoRR abs/1606.04930. 

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. 

LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521, 436–444. 

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. 

