DeepBach: a Steerable Model for Bach Chorales Generation

Stochastic relaxation, gibbs distributions, and the bayesian restoration of images

Geman, Stuart and Geman, Donald · 1984

Earlier work this paper cites.

389 Chorales (Choral-Gesange): SATB (German Language Edition)

Bach, J.S · 1985

Earlier work this paper cites.

An expert system for harmonizing four-part chorales

Ebcioglu, Kemal · 1988

Earlier work this paper cites.

Harmonet: A neural net for harmonizing chorales in the style of js bach

Hild, Hermann, Feulner, Johannes, and Menzel, Wolfram · 1992

Earlier work this paper cites.

Long short-term memory

Hochreiter, Sepp and Schmidhuber, Jürgen · 1997

Earlier work this paper cites.

Dependency networks for inference, collaborative filtering, and data visualization

Heckerman, David, Chickering, David Maxwell, Meek, Christopher, Rounthwaite, Robert, and Kadie, Carl · 2000

Earlier work this paper cites.

Harmonising chorales by probabilistic inference

Allan, Moray and Williams, Christopher KI · 2005

Earlier work this paper cites.

Statistical Mechanics: Algorithms and Computations

Krauth, W · 2006

Earlier work this paper cites.

music21: A toolkit for computer-aided musicology and symbolic music data

Cuthbert, Michael Scott and Ariza, Christopher · 2010

Earlier work this paper cites.

Rectified linear units improve restricted boltzmann machines

Nair, Vinod and Hinton, Geoffrey E · 2010

Cited alongside, same era.

Reversibility and stochastic networks

Kelly, Frank P · 2011

Cited alongside, same era.

Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription

Boulanger-Lewandowski, Nicolas, Bengio, Yoshua, and Vincent, Pascal · 2012

Cited alongside, same era.

How to Construct Deep Recurrent Neural Networks

Pascanu, R., Gulcehre, C., Cho, K., and Bengio, Y · 2013

Cited alongside, same era.

Multiple viewpoint systems: Time complexity and the construction of domains for complex musical viewpoints in the harmonization problem

Whorley, Raymond P, Wiggins, Geraint A, Rhodes, Christophe, and Pearce, Marcus T · 2013

Cited alongside, same era.

Behaviour of the gibbs sampler when conditional distributions are potentially incompatible

TensorFlow: Large-scale machine learning on heterogeneous systems, 2015

Abadi, Martín, Agarwal, Ashish, Barham, Paul, Brevdo, Eugene, Chen, Zhifeng, Citro, Craig, Corrado, Greg S., Davis, Andy, Dean, Jeffrey, Devin, Matthieu, Ghemawat, Sanjay, Goodfellow, Ian, Harp, Andrew, Irving, Geoffrey, Isard, Michael, Jia, Yangqing, Jozefowicz, Rafal, Kaiser, Lukasz, Kudlur, Manjunath, Levenberg, Josh, Mané, Dan, Monga, Rajat, Moore, Sherry, Murray, Derek, Olah, Chris, Schuster, Mike, Shlens, Jonathon, Steiner, Benoit, Sutskever, Ilya, Talwar, Kunal, Tucker, Paul, Vanhoucke, Vincent, Vasudevan, Vijay, Viégas, Fernanda, Vinyals, Oriol, Warden, Pete, Wattenberg, Martin, Wicke, Martin, Yu, Yuan, and Zheng, Xiaoqiang · 2015

Later among the works it cites.

Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition

Li, Xiangang and Wu, Xihong · 2015

Later among the works it cites.

Modelling high-dimensional sequences with lstm-rtrbm: application to polyphonic music generation

Lyu, Qi, Wu, Zhiyong, Zhu, Jun, and Meng, Helen · 2015

Later among the works it cites.

Maximum entropy model for melodic patterns

Sakellariou, J., Tria, F., Loreto, V., and Pachet, F · 2015

Later among the works it cites.

Ensuring rapid mixing and low bias for asynchronous gibbs sampling