35 - Deep Learning - Plain Version 2020/ClipID:21169 previous clip next clip

Recording date 2020-10-12

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Free

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English

Organisational Unit

Friedrich-Alexander-Universität Erlangen-Nürnberg

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Friedrich-Alexander-Universität Erlangen-Nürnberg

Deep Learning - Recurrent Neural Networks Part 5

This video explains sequence generation using RNNs.

For reminders to watch the new video follow on Twitter or LinkedIn.

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

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Maier, Andreas
Prof. Dr. Andreas Maier
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Maier, Andreas
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Maier, Andreas
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Maier, Andreas
Prof. Dr. Andreas Maier
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Maier, Andreas
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