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The polyphonic nature of music makes the application of deep learning to music modelling a challenging task.
Auto-encoding variational bayes
D. Kingma and M. Welling · 2013
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Generative adversarial networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey · 2015
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Learning-based methods for comparing sequences, with applications to audio-to-midi alignment and matching
C. Raffel · 2016
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks
L. Mescheder, S. Nowozin, and A. Geiger · 2017
Cited alongside, same era.
A hierarchical latent vector model for learning long-term structure in music
A. Roberts, J. Engel, C. Raffel, C. Hawthorne, and D. Eck · 2018
Cited alongside, same era.
Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment
H. Dong, W. Hsiao, L. Yang, and Y. Yang · 2018
Cited alongside, same era.
Transformer-xl: Attentive language models beyond a fixed-length context
Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. Le, and R. Salakhutdinov · 2019
Cited alongside, same era.
Scheduled sampling for transformers
T. Mihaylova and A. Martins · 2019
Later among the works it cites.
Learning style-aware symbolic music representations by adversarial autoencoders
A. Valenti, A. Carta, and D. Bacciu · 2020
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Pop music transformer: Beat-based modeling and generation of expressive pop piano compositions
Y. Huang and Y. Yang · 2020
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Encoding musical style with transformer autoencoders
K. Choi, C. Hawthorne, I. Simon, M. Dinculescu, and J. Engel · 2020
Later among the works it cites.
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