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A large number of objectives have been proposed to train latent variable generative models.
Introduction to linear algebra , volume 3
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The im algorithm: a variational approach to information maximization
Barber, David and Agakov, Felix · 2003
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Convex optimization
Boyd, Stephen and Vandenberghe, Lieven · 2004
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On divergences and informations in statistics and information theory
Liese, Friedrich and Vajda, Igor · 2006
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A kernel method for the two-sample-problem
Gretton, Arthur, Borgwardt, Karsten M, Rasch, Malte, Schölkopf, Bernhard, and Smola, Alex J · 2007
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Learning and generalization with the information bottleneck
Shamir, Ohad, Sabato, Sivan, and Tishby, Naftali · 2010
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Auto-Encoding Variational Bayes
Kingma, D. P and Welling, M · 2013
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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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On the convergence to saddle points of concave-convex functions, the gradient method and emergence of oscillations
Holding, Thomas and Lestas, Ioannis · 2014
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Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2014
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D., Mohamed, S., and Wierstra, D · 2014
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Makhzani, Alireza, Shlens, Jonathon, Jaitly, Navdeep, and Goodfellow, Ian · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, Alec, Metz, Luke, and Chintala, Soumith · 2015
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On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions
Ramdas, Aaditya, Reddi, Sashank Jakkam, Póczos, Barnabás, Singh, Aarti, and Wasserman, Larry A · 2015
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Deep learning and the information bottleneck principle
Tishby, Naftali and Zaslavsky, Noga · 2015
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Learning to discover cross-domain relations with generative adversarial networks
Kim, Taeksoo, Cha, Moonsu, Kim, Hyunsoo, Lee, Jung Kwon, and Kim, Jiwon · 2017
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Deep hybrid models: Bridging discriminative and generative approaches
Kuleshov, Volodymyr and Ermon, Stefano · 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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Adversarial symmetric variational autoencoder
Pu, Yuchen, Wang, Weiyao, Henao, Ricardo, Chen, Liqun, Gan, Zhe, Li, Chunyuan, and Carin, Lawrence · 2017
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Tolstikhin, Ilya, Bousquet, Olivier, Gelly, Sylvain, and Schoelkopf, Bernhard · 2017
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Donahue, Jeff, Krähenbühl, Philipp, and Darrell, Trevor · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, Irina, Matthey, Loic, Pal, Arka, Burgess, Christopher, Glorot, Xavier, Botvinick, Matthew, Mohamed, Shakir, and Lerchner, Alexander · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Liu, Qiang and Wang, Dilin · 2016
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Learning in implicit generative models
Mohamed, Shakir and Lakshminarayanan, Balaji · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, Sebastian, Cseke, Botond, and Tomioka, Ryota · 2016
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An information-theoretic analysis of deep latent-variable models
Alemi, Alexander A., Poole, Ben, Fischer, Ian, Dillon, Joshua V., Saurous, Rif A., and Murphy, Kevin · 2017
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Wasserstein GAN
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, Xi, Duan, Yan, Houthooft, Rein, Schulman, John, Sutskever, Ilya, and Abbeel, Pieter
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Improved variational autoencoders for text modeling using dilated convolutions
Yang, Zichao, Hu, Zhiting, Salakhutdinov, Ruslan, and Berg-Kirkpatrick, Taylor · 2017
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Infovae: Information maximizing variational autoencoders
Zhao, Shengjia, Song, Jiaming, and Ermon, Stefano · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, Jun-Yan, Park, Taesung, Isola, Phillip, and Efros, Alexei A · 2017
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Sparse-gen: Modeling sparse deviations for compressed sensing using generative models
Dhar, Manik, Grover, Aditya, and Ermon, Stefano · 2018
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Flow-GAN: Combining maximum likelihood and adversarial learning in generative models
Grover, Aditya, Dhar, Manik, and Ermon, Stefano · 2018
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On convergence and stability of gans
Kodali, Naveen, Hays, James, Abernethy, Jacob, and Kira, Zsolt · 2018
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