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Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning.
Semi-supervised clustering by seeding
Basu, Sugato, Banerjee, Arindam, and Mooney, Raymond J · 2002
Earlier work this paper cites.
Theory-based Bayesian models of inductive learning and reasoning
Tenenbaum, Joshua B., Griffiths, Thomas L., and Kemp, Charles · 2006
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Theano: new features and speed improvements
Bastien, Frédéric, Lamblin, Pascal, Pascanu, Razvan, Bergstra, James, Goodfellow, Ian J., Bergeron, Arnaud, Bouchard, Nicolas, and Bengio, Yoshua · 2012
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Representation learning: A review and new perspectives
Bengio, Yoshua, Courville, Aaron, and Vincent, Pascal · 2013
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, Diederik P; Welling, Max · 2013
Earlier work this paper cites.
One-shot learning by inverting a compositional causal process
Lake, Brenden M, Salakhutdinov, Ruslan R, and Tenenbaum, Josh · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, Diederik and Ba, Jimmy · 2014
Earlier work this paper cites.
Semi-Supervised Learning with Deep Generative Models
Kingma, Diederik P., Rezende, Danilo Jimenez, Mohamed, Shakir, and Welling, Max · 2014
Earlier work this paper cites.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, Danilo J., Mohamed, Shakir, and Wierstra, Daan · 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
Cited alongside, same era.
Lasagne: First release., August 2015
Dieleman, Sander, Schlüter, Jan, Raffel, Colin, Olson, Eben, Sønderby, Søren K, Nouri, Daniel, van den Oord, Aaron, and and, Eric Battenberg · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
Cited alongside, same era.
Krishnan, Rahul G, Shalit, Uri, and Sontag, David · 2015
Cited alongside, same era.
Distributional Smoothing with Virtual Adversarial Training
Miyato, Takeru, Maeda, Shin-ichi, Koyama, Masanori, Nakae, Ken, and Ishii, Shin · 2015
PixelVAE: A latent variable model for natural images
Gulrajani, Ishaan, Kumar, Kundan, Ahmed, Faruk, Ali Taiga, Adrien, Visin, Francesco, Vazquez, David, and Courville, Aaron · 2016
Later among the works it cites.
Categorical reparameterization with gumbel-softmax
Jang, Eric, Gu, Shixiang, and Poole, Ben · 2016
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Improved variational inference with inverse autoregressive flow
Kingma, Diederik P, Salimans, Tim, Jozefowicz, Rafal, Chen, Xi, Sutskever, Ilya, and Welling, Max · 2016
Later among the works it cites.
Auxiliary Deep Generative Models
Maaløe, Lars, Sønderby, Casper K., Sønderby, Søren K., and Winther, Ole · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, Chris J., Mnih, Andriy, and Teh, Yee Whye · 2016
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Cited alongside, same era.
Hierarchical variational models
Ranganath, Rajesh, Tran, Dustin, and Blei, David M · 2015
Cited alongside, same era.
Semi-supervised learning with ladder networks
Rasmus, Antti, Berglund, Mathias, Honkala, Mikko, Valpola, Harri, and Raiko, Tapani · 2015
Cited alongside, same era.
Variational Inference with Normalizing Flows
Rezende, Danilo Jimenez and Mohamed, Shakir · 2015
Cited alongside, same era.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
Springenberg, J.T · 2015
Cited alongside, same era.
Sequential neural models with stochastic layers
Fraccaro, Marco, Sønderby, Søren Kaae, Paquet, Ulrich, and Winther, Ole · 2016
Cited alongside, same era.
Importance Weighted Autoencoders
Burda, Yuri, Grosse, Roger, and Salakhutdinov, Ruslan
Cited in the paper.
Accurate and conservative estimates of mrf log-likelihood using reverse annealing
Burda, Yuri, Grosse, Roger, and Salakhutdinov, Ruslan
Cited in the paper.
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Later among the works it cites.
Ladder variational autoencoders
Sønderby, Casper Kaae, Raiko, Tapani, Maaløe, Lars, Sønderby, Søren Kaae, and Winther, Ole · 2016
Later among the works it cites.
Variational Gaussian process
Tran, Dustin, Ranganath, Rajesh, and Blei, David M · 2016
Later among the works it cites.
Variational Lossy Autoencoder
Chen, Xi, Kingma, Diederik P., Salimans, Tim, Duan, Yan, Dhariwal, Prafulla, Schulman, John, Sutskever, Ilya, and Abbeel, Pieter · 2017
Closest in time.
Discrete variational autoencoders
Rolfe, Jason Tyler · 2017
Closest in time.