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Generative adversarial networks (GANs) are a powerful approach to unsupervised learning.
Maximum entropy generators for energy-based models
Kumar, R., Goyal, A., Courville, A., and Bengio, Y. (2019) · 1901
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Out-of-sample testing for gans
Sánchez-Martín, P., Olmos, P. M., and Pérez-Cruz, F. (2019) · 1901
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Semi-implicit generative model
Yin, M. and Zhou, M. (2019) · 1905
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Reweighted expectation maximization
Dieng, A. B. and Paisley, J. (2019) · 1906
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016) · 1937
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On estimation of a probability density function and mode
Parzen, E. (1962) · 1962
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Doubly stochastic variational bayes for non-conjugate inference
Titsias, M. and Lázaro-Gredilla, M. (2014) · 1979
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Monte Carlo methods of inference for implicit statistical models
Diggle, P. J. and Gratton, R. J. (1984) · 1984
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Bayesian neural networks and density networks
MacKay, D. J. (1995) · 1995
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A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y. and Schapire, R. E. (1997) · 1997
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Learning in graphical models
Jordan, M. I. (1998) · 1998
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Principal information theoretic approaches
Soofi, E. S. (2000) · 2000
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Annealed importance sampling
Neal, R. M. (2001) · 2001
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Probability theory: The logic of science
Jaynes, E. T. (2003) · 2003
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Divergence measures and message passing
Minka, T. et al. (2005) · 2005
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Pattern recognition and machine learning
Bishop, C. M. (2006) · 2006
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Graphical models, exponential families, and variational inference
Wainwright, M. J., Jordan, M. I., et al. (2008) · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
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Mcmc using hamiltonian dynamics
Neal, R. M. et al. (2011) · 2011
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
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Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., and Bengio, S. (2015) · 2015
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Training generative neural networks via maximum mean discrepancy optimization
Dziugaite, G. K., Roy, D. M., and Ghahramani, Z. (2015) · 2015
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Generative moment matching networks
Li, Y., Swersky, K., and Zemel, R. (2015) · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
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Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. (2015) · 2015
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Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks
Mescheder, L., Nowozin, S., and Geiger, A. (2017) · 2017
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Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J. (2017) · 2017
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Variational approaches for auto-encoding generative adversarial networks
Rosca, M., Lakshminarayanan, B., Warde-Farley, D., and Mohamed, S. (2017) · 2017
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Veegan: Reducing mode collapse in gans using implicit variational learning
Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., and Sutton, C. (2017) · 2017
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Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B. (2017) · 2017
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Radford, A., Metz, L., and Chintala, S. (2015) · 2015
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P. (2015) · 2015
Cited alongside, same era.
Donahue, J., Krähenbühl, P., and Darrell, T. (2016) · 2016
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Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., and Courville, A. (2016) · 2016
Cited alongside, same era.
Instance noise: a trick for stabilising gan training
Huszár, F. (2016) · 2016
Cited alongside, same era.
Learning in implicit generative models
Mohamed, S. and Lakshminarayanan, B. (2016) · 2016
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R. (2016) · 2016
Cited alongside, same era.
On the discrimination-generalization tradeoff in gans
Zhang, P., Liu, Q., Zhou, D., Xu, T., and He, X. (2017) · 2017
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Do gans learn the distribution? some theory and empirics
Arora, S., Risteski, A., and Zhang, Y. (2018) · 2018
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Discriminator rejection sampling
Azadi, S., Olsson, C., Darrell, T., Goodfellow, I., and Odena, A. (2018) · 2018
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Entropic GANs meet VAEs: A statistical approach to compute sample likelihoods in gans
Balaji, Y., Hassani, H., Chellappa, R., and Feizi, S. (2018) · 2018
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Mine: mutual information neural estimation
Belghazi, M. I., Baratin, A., Rajeswar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, R. D. (2018) · 2018
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Bińkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A. (2018) · 2018
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K. (2018) · 2018
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Improving gan training via binarized representation entropy (bre) regularization
Cao, Y., Ding, G. W., Lui, K. Y.-C., and Huang, R. (2018) · 2018
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Flow-gan: Combining maximum likelihood and adversarial learning in generative models
Grover, A., Dhar, M., and Ermon, S. (2018) · 2018
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Pacgan: The power of two samples in generative adversarial networks
Lin, Z., Khetan, A., Fanti, G., and Oh, S. (2018) · 2018
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Learning implicit generative models with the method of learned moments
Ravuri, S., Mohamed, S., Rosca, M., and Vinyals, O. (2018) · 2018
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Entropic optimal transport is maximum-likelihood deconvolution
Rigollet, P. and Weed, J. (2018) · 2018
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Unbiased implicit variational inference
Titsias, M. K. and Ruiz, F. J. (2018) · 2018
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Metropolis-hastings generative adversarial networks
Turner, R., Hung, J., Saatci, Y., and Yosinski, J. (2018) · 2018
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It takes (only) two: Adversarial generator-encoder networks
Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2018) · 2018
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BourGAN: Generative networks with metric embeddings
Xiao, C., Zhong, P., and Zheng, C. (2018) · 2018
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T. (2019) · 2019
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