Fetching the paper…
Reading the bibliography…
We consider the problem of learning high-level controls over the global structure of generated sequences, particularly in the context of symbolic music generation with complex language models.
Huang, C.-Z. A., Cooijmans, T., Roberts, A., Courville, A., and Eck, D · 1903
Earlier work this paper cites.
Query by humming: musical information retrieval in an audio database
Ghias, A., Logan, J., Chamberlin, D., and Smith, B. C · 1995
Earlier work this paper cites.
A large-scale evaluation of acoustic and subjective music-similarity measures
Berenzweig, A., Logan, B., Ellis, D. P., and Whitman, B · 2004
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R · 2006
Earlier work this paper cites.
Learning a metric for music similarity
Slaney, M., Weinberger, K., and White, W · 2008
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008
Earlier work this paper cites.
Deep boltzmann machines
Salakhutdinov, R. and Hinton, G · 2009
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A · 2010
Earlier work this paper cites.
Automatic tagging of audio: The state-of-the-art
Bertin-Mahieux, T., Eck, D., and Mandel, M · 2011
Earlier work this paper cites.
Autoencoders, unsupervised learning, and deep architectures
Baldi, P · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
Earlier work this paper cites.
Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., and Bengio, S · 2015
Earlier work this paper cites.
Learning structured output representation using deep conditional generative models
Sohn, K., Lee, H., and Yan, X · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
Cited alongside, same era.
Wavenet: A generative model for raw audio
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Ladder variational autoencoders
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O · 2016
Cited alongside, same era.
Conditional image generation with pixelcnn decoders
Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al · 2016
Cited alongside, same era.
A universal music translation network
Mor, N., Wolf, L., Polyak, A., and Taigman, Y · 2018
Later among the works it cites.
This time with feeling: learning expressive musical performance
Oore, S., Simon, I., Dieleman, S., Eck, D., and Simonyan, K · 2018
Later among the works it cites.
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, Ł., Shazeer, N., Ku, A., and Tran, D · 2018
Later among the works it cites.
A hierarchical latent vector model for learning long-term structure in music
Roberts, A., Engel, J., Raffel, C., Hawthorne, C., and Eck, D · 2018
Later among the works it cites.
Self-attention with relative position representations
Shaw, P., Uszkoreit, J., and Vaswani, A · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Waite, E · 2016
Cited alongside, same era.
On the evaluation of generative models in music
Yang, L.-C. and Lerch, A · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
A neural representation of sketch drawings
Ha, D. and Eck, D · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
Cited alongside, same era.
Few-shot autoregressive density estimation: Towards learning to learn distributions
Reed, S., Chen, Y., Paine, T., Oord, A. v. d., Eslami, S., Rezende, D., Vinyals, O., and de Freitas, N · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Later among the works it cites.
Gansynth: Adversarial neural audio synthesis
Engel, J., Agrawal, K. K., Chen, S., Gulrajani, I., Donahue, C., and Roberts, A · 2019
Closest in time.
Learning to groove with inverse sequence transformations
Gillick, J., Roberts, A., Engel, J., Eck, D., and Bamman, D · 2019
Closest in time.
Enabling factorized piano music modeling and generation with the MAESTRO dataset
Hawthorne, C., Stasyuk, A., Roberts, A., Simon, I., Huang, C.-Z. A., Dieleman, S., Elsen, E., Engel, J., and Eck, D · 2019
Closest in time.
Improving automatic jazz melody generation by transfer learning techniques
Hung, H.-T., Wang, C.-Y., Yang, Y.-H., and Wang, H.-M · 2019
Closest in time.
Exploring conditioning for generative music systems with human-interpretable controls
Meade, N., Barreyre, N., Lowe, S. C., and Oore, S · 2019
Closest in time.
Musenet, 2019
Payne, C · 2019
Closest in time.
Generating piano music with transformer
Simon, I., Huang, C.-Z. A., Engel, J., Hawthorne, C., and Dinculescu, M · 2019
Closest in time.
T-cvae: Transformer-based conditioned variational autoencoder for story completion
Wang, T. and Wan, X · 2019
Closest in time.