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Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood.
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Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2016
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Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 2017
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Mikołaj Bińkowski, Dougal J Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Tian Qi Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
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Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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BEGAN: Boundary equilibrium generative adversarial networks
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Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Carl Johann Simon-Gabriel, and Bernhard Schölkopf · 2017
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Many paths to equilibrium: GANs do not need to decrease a divergence at every step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M Dai, Shakir Mohamed, and Ian Goodfellow · 2017
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Improved training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Connections with robust PCA and the role of emergent sparsity in variational autoencoder models
Bin Dai, Yu Wang, John Aston, Gang Hua, and David Wipf · 2018
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Hyperspherical variational auto-encoders
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Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2018
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Jakub Tomczak and Max Welling · 2018
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Rianne van den Berg, Leonard Hasenclever, Jakub M Tomczak, and Max Welling · 2018
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Adversarially regularized autoencoders
Junbo Zhao, Yoon Kim, Kelly Zhang, Alexander Rush, and Yann LeCun · 2018
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Diagnosing and enhancing vae models
Bin Dai and David Wipf · 2019
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