Deep Learning - Recurrent Neural Networks Part 5

This video explains sequence generation using RNNs.

Video References:
Folk Session 1166

RNN Folk Music
FolkRNN.org
MachineFolkSession.com
The Glass Herry Comment 14128

Links
Character RNNs
CNNs for Machine Translation
Composing Music with RNNs

References
[1] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. “Neural Machine Translation by Jointly Learning to Align and Translate”. In: CoRR abs/1409.0473 (2014). arXiv: 1409.0473.
[2] Yoshua Bengio, Patrice Simard, and Paolo Frasconi. “Learning long-term dependencies with gradient descent is difficult”. In: IEEE transactions on neural networks 5.2 (1994), pp. 157–166.
[3] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling”. In: arXiv preprint arXiv:1412.3555 (2014).
[4] Douglas Eck and Jürgen Schmidhuber. “Learning the Long-Term Structure of the Blues”. In: Artificial Neural Networks — ICANN 2002. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002, pp. 284–289.
[5] Jeffrey L Elman. “Finding structure in time”. In: Cognitive science 14.2 (1990), pp. 179–211.
[6] Jonas Gehring, Michael Auli, David Grangier, et al. “Convolutional Sequence to Sequence Learning”. In: CoRR abs/1705.03122 (2017). arXiv: 1705.03122.
[7] Alex Graves, Greg Wayne, and Ivo Danihelka. “Neural Turing Machines”. In: CoRR abs/1410.5401 (2014). arXiv: 1410.5401.
[8] Karol Gregor, Ivo Danihelka, Alex Graves, et al. “DRAW: A Recurrent Neural Network For Image Generation”. In: Proceedings of the 32nd International Conference on Machine Learning. Vol. 37. Proceedings of Machine Learning Research. Lille, France: PMLR, July 2015, pp. 1462–1471.
[9] Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, et al. “Learning phrase representations using RNN encoder-decoder for statistical machine translation”. In: arXiv preprint arXiv:1406.1078 (2014).
[10] J J Hopfield. “Neural networks and physical systems with emergent collective computational abilities”. In: Proceedings of the National Academy of Sciences 79.8 (1982), pp. 2554–2558. eprint: http://www.pnas.org/content/79/8/2554.full.pdf.
[11] W.A. Little. “The existence of persistent states in the brain”. In: Mathematical Biosciences 19.1 (1974), pp. 101–120.
[12] Sepp Hochreiter and Jürgen Schmidhuber. “Long short-term memory”. In: Neural computation 9.8 (1997), pp. 1735–1780.
[13] Volodymyr Mnih, Nicolas Heess, Alex Graves, et al. “Recurrent Models of Visual Attention”. In: CoRR abs/1406.6247 (2014). arXiv: 1406.6247.
[14] Bob Sturm, João Felipe Santos, and Iryna Korshunova. “Folk music style modelling by recurrent neural networks with long short term memory units”. eng. In: 16th International Society for Music Information Retrieval Conference, late-breaking Malaga, Spain, 2015, p. 2.
[15] Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, et al. “End-to-End Memory Networks”. In: CoRR abs/1503.08895 (2015). arXiv: 1503.08895.
[16] Peter M. Todd. “A Connectionist Approach to Algorithmic Composition”. In: 13 (Dec. 1989).
[17] Ilya Sutskever. “Training recurrent neural networks”. In: University of Toronto, Toronto, Ont., Canada (2013).
[18] Andrej Karpathy. “The unreasonable effectiveness of recurrent neural networks”. In: Andrej Karpathy blog (2015).
[19] Jason Weston, Sumit Chopra, and Antoine Bordes. “Memory Networks”. In: CoRR abs/1410.3916 (2014). arXiv: 1410.3916.

Further Reading:
A gentle Introduction to Deep Learning