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Chapter 8

4. Hadjeres, G., Pachet, F., Nielsen, F. (August 2017). DeepBach: a Steerable Model

for Bach Chorales Generation. Proceedings of the 34th International Conference

on Machine Learning (ICML).

5. Karpathy, A. (2015). The Unreasonable Effectiveness of Recurrent Neural

Networks. URL: http://karpathy.github.io/2015/05/21/rnneffectiveness/.

6. Karpathy, A., Li, F. (2015). Deep Visual-Semantic Alignments for Generating

Image Descriptions. Conference on Pattern Recognition and Pattern

Recognition (CVPR).

7. Socher, et al. (2013). Recursive Deep Models for Sentiment Compositionality

over a Sentiment Treebank. Proceedings of the 2013 Conference on Empirical

Methods in Natural Language Processing (EMNLP).

8. Bahdanau, D., Cho, K., Bengio, Y. (2015). Neural Machine Translation by Jointly

Learning to Align and Translate. arXiv: 1409.0473 [cs.CL].

9. Wu, Y., et al. (2016). Google's Neural Machine Translation System: Bridging the

Gap between Human and Machine Translation. arXiv 1609.08144 [cs.CL].

10. Vinyals, O., et al. (2015). Grammar as a Foreign Language. Advances in Neural

Information Processing Systems (NIPS).

11. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1985). Learning Internal

Representations by Error Propagation. Parallel Distributed Processing:

Explorations in the Microstructure of Cognition.

12. Britz, D. (2015). Recurrent Neural Networks Tutorial, Part 3 - Backpropagation

Through Time and Vanishing Gradients. URL: http://www.wildml.

com/2015/10/recurrent-neural-networks-tutorial-part-3-

backpropagation-through-time-and-vanishing-gradients/.

13. Pascanu, R., Mikolov, T., and Bengio, Y. (2013). On the difficulty of training

Recurrent Neural Networks. Proceedings of the 30th International Conference

on Machine Learning (ICML).

14. Hochreiter, S., and Schmidhuber, J. (1997). LSTM can solve hard long time lag

problems. Advances in Neural Information Processing Systems (NIPS).

15. Britz, D. (2015). Recurrent Neural Network Tutorial, Part 4 – Implementing

a GRU/LSTM RNN with Python and Theano. URL: http://www.wildml.

com/2015/10/recurrent-neural-network-tutorial-part-4-

implementing-a-grulstm-rnn-with-python-and-theano/.

16. Olah, C. (2015). Understanding LSTM Networks. URL: https://colah.

github.io/posts/2015-08-Understanding-LSTMs/.

17. Cho, K., et al. (2014). Learning Phrase Representations using RNN Encoder-

Decoder for Statistical Machine Translation. arXiv: 1406.1078 [cs.CL].

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