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We introduce and motivate generative modeling as a central task for machine learning and provide a critical view of the algorithms which have been proposed for solving this task.
A learning algorithm for continually running fully recurrent neural networks
Williams, R. J. and D. Zipser (1989) · 1989
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Annealed Importance Sampling
Neal, R. M. (1998, March) · 1998
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University Lecture http://nowak.ece.wisc.edu/SLT09/lecture13.pdf
Nowak, R. (2009) · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and A. Hyvärinen (2010, 13–15 May) · 2010
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Better mixing via deep representations
Bengio, Y., G. Mesnil, Y. Dauphin, and S. Rifai (2012) · 2012
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Sequence transduction with recurrent neural networks
Graves, A. (2012) · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., N. Léonard, and A. Courville (2013) · 2013
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Auto-Encoding Variational Bayes
Kingma, D. P. and M. Welling (2013, December) · 2013
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Accurate and conservative estimates of MRF log-likelihood using reverse annealing
Burda, Y., R. B. Grosse, and R. Salakhutdinov (2014) · 2014
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Generative Adversarial Networks
Goodfellow, I. J., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio (2014, June) · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., O. Vinyals, N. Jaitly, and N. Shazeer (2015) · 2015
Cited alongside, same era.
How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary ?
Huszár, F. (2015, November) · 2015
Cited alongside, same era.
Professor Forcing: A New Algorithm for Training Recurrent Networks
Lamb, A., A. Goyal, Y. Zhang, S. Zhang, A. Courville, and Y. Bengio (2016, October) · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T., I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen (2016) · 2016
Cited alongside, same era.
Towards principled methods for training generative adversarial networks
Arjovsky, M. and L. Bottou (2017) · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a nash equilibrium
Heusel, M., H. Ramsauer, T. Unterthiner, B. Nessler, G. Klambauer, and S. Hochreiter (2017) · 2017
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Progressive growing of gans for improved quality, stability, and variation
Karras, T., T. Aila, S. Laine, and J. Lehtinen (2017) · 2017
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Gibbsnet: Iterative adversarial inference for deep graphical models
Lamb, A., D. Hjelm, Y. Ganin, J. P. Cohen, A. Courville, and Y. Bengio (2017) · 2017
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Salimans, T., A. Karpathy, X. Chen, and D. P. Kingma (2017) · 2017
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Wasserstein GAN
Arjovsky, M., S. Chintala, and L. Bottou (2017, January) · 2017
Cited alongside, same era.
Adversarially learned inference
Dumoulin, V., I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville (2017) · 2017
Cited alongside, same era.
Elgammal, A. M., B. Liu, M. Elhoseiny, and M. Mazzone (2017) · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I., F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville (2017) · 2017
Cited alongside, same era.
A neural algorithm of artistic style
Gatys, L. A., A. S. Ecker, and M. Bethge (2015a)
Cited in the paper.
Gatys, L. A., A. S. Ecker, and M. Bethge (2015b)
Cited in the paper.
Ulyanov, D., A. Vedaldi, and V. S. Lempitsky (2017) · 2017
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A Note on the Inception Score
Barratt, S. and R. Sharma (2018, January) · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., T. Kataoka, M. Koyama, and Y. Yoshida (2018) · 2018
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Is Generator Conditioning Causally Related to GAN Performance ?
Odena, A., J. Buckman, C. Olsson, T. B. Brown, C. Olah, C. Raffel, and I. Goodfellow (2018, February) · 2018
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Stabilizing training of generative adversarial networks through regularization
Roth, K., A. Lucchi, S. Nowozin, and T. Hofmann (2017) · 2028
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