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The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, Ronald J · 1992
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Bleu: a method for automatic evaluation of machine translation
Papineni, Kishore, Roukos, Salim, Ward, Todd, and Zhu, Wei-Jing · 2002
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Framewise phoneme classification with bidirectional lstm and other neural network architectures
Graves, Alex and Schmidhuber, Jürgen · 2005
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Natural language processing (almost) from scratch
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., and Kuksa, P · 2011
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Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., and Bengio, Y · 2012
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A kernel two-sample test
Gretton, Arthur, Borgwardt, Karsten M, Rasch, Malte J, Schölkopf, Bernhard, and Smola, Alexander · 2012
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
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Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
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Convolutional neural network architectures for matching natural language sentences
Hu, B., Lu, Z., Li, H., and Chen, Q · 2014
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A convolutional neural network for modelling sentences
Kalchbrenner, N., Grefenstette, E., and Blunsom, P · 2014
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Convolutional neural networks for sentence classification
Kim, Y · 2014
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Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2014
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Conditional generative adversarial nets
Mirza, Mehdi and Osindero, Simon · 2014
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Ramdas, Aaditya, Reddi, Sashank J, Poczos, Barnabas, Singh, Aarti, and Wasserman, Larry · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, Samy, Vinyals, Oriol, Jaitly, Navdeep, and Shazeer, Noam · 2015
Cited alongside, same era.
Training generative neural networks via maximum mean discrepancy optimization
Dziugaite, Gintare Karolina, Roy, Daniel M, and Ghahramani, Zoubin · 2015
Cited alongside, same era.
How (not) to train your generative model: Scheduled sampling, likelihood, adversary?
Huszár, Ferenc · 2015
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Effective use of word order for text categorization with convolutional neural networks
Johnson, R. and Zhang, T · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, Sebastian, Cseke, Botond, and Tomioka, Ryota · 2016
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Variational autoencoder for deep learning of images, labels and captions
Pu, Yunchen, Gan, Zhe, Henao, Ricardo, Yuan, Xin, Li, Chunyuan, Stevens, Andrew, and Carin, Lawrence · 2016
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Improved techniques for training gans
Salimans, Tim, Goodfellow, Ian, Zaremba, Wojciech, Cheung, Vicki, Radford, Alec, and Chen, Xi · 2016
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A note on the evaluation of generative models
Theis, Lucas, Oord, Aäron van den, and Bethge, Matthias · 2016
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Learning to draw samples: With application to amortized mle for generative adversarial learning
Wang, Dilin and Liu, Qiang · 2016
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Generative moment matching networks
Li, Yujia, Swersky, Kevin, and Zemel, Richard S · 2015
Cited alongside, same era.
Makhzani, Alireza, Shlens, Jonathon, Jaitly, Navdeep, Goodfellow, Ian, and Frey, Brendan · 2015
Cited alongside, same era.
Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., and Fidler, S · 2015
Cited alongside, same era.
Generating sentences from a continuous space
Bowman, Samuel R, Vilnis, Luke, Vinyals, Oriol, Dai, Andrew M, Jozefowicz, Rafal, and Bengio, Samy · 2016
Cited alongside, same era.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, Xi, Duan, Yan, Houthooft, Rein, Schulman, John, Sutskever, Ilya, and Abbeel, Pieter · 2016
Cited alongside, same era.
Adversarially learned inference
Dumoulin, Vincent, Belghazi, Ishmael, Poole, Ben, Lamb, Alex, Arjovsky, Martin, Mastropietro, Olivier, and Courville, Aaron · 2016
Cited alongside, same era.
Unsupervised learning of sentence representations using convolutional neural networks
Gan, Zhe, Pu, Yunchen, Henao, Ricardo, Li, Chunyuan, He, Xiaodong, and Carin, Lawrence · 2016
Cited alongside, same era.
Zhang, Yizhe, Gan, Zhe, and Carin, Lawrence · 2016
Later among the works it cites.
Towards principled methods for training generative adversarial networks
Arjovsky, Martin and Bottou, Léon · 2017
Closest in time.
Wasserstein gan
Arjovsky, Martin, Chintala, Soumith, and Bottou, Léon · 2017
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Adversarial feature learning
Donahue, Jeff, Krähenbühl, Philipp, and Darrell, Trevor · 2017
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Adversarial learning for neural dialogue generation
Li, Jiwei, Monroe, Will, Shi, Tianlin, Ritter, Alan, and Jurafsky, Dan · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, Chris J, Mnih, Andriy, and Teh, Yee Whye · 2017
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Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks
Mescheder, Lars, Nowozin, Sebastian, and Geiger, Andreas · 2017
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Unrolled generative adversarial networks
Metz, Luke, Poole, Ben, Pfau, David, and Sohl-Dickstein, Jascha · 2017
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Seqgan: sequence generative adversarial nets with policy gradient
Yu, Lantao, Zhang, Weinan, Wang, Jun, and Yu, Yong · 2017
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Energy-based generative adversarial network
Zhao, Junbo, Mathieu, Michael, and LeCun, Yann · 2017
Closest in time.