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Deep generative modeling has seen impressive advances in recent years, to the point where it is now commonplace to see simulated samples (e.g., images) that closely resemble real-world data.
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Werner Braun and K Hepp · 1977
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Optimal transport: old and new , volume 338
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and M. Welling · 2014
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Variational inference with normalizing flows
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Normalizing flows on Riemannian manifolds
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Improved techniques for training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Two numerical approaches to stationary mean-field games
Noha Almulla, Rita Ferreira, and Diogo Gomes · 2017
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Improved training of Wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C 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
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Deep generative learning via variational gradient flow
Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, and Shunkang Zhang · 2019
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Bias correction of learned generative models using likelihood-free importance weighting
Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric J Horvitz, and Stefano Ermon · 2019
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Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, and Fabian-Robert Stöter · 2019
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Sobolev descent
Youssef Mroueh, Tom Sercu, and Anant Raj · 2019
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Generative modeling by estimating gradients of the data distribution
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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MMDGAN: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
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Stein variational gradient descent as gradient flow
Qiang Liu · 2017
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{ \{ Euclidean, metric, and Wasserstein } \} gradient flows: an overview
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Demystifying MMD GANs
Mikołaj Bińkowski, Dougal J Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Variational rejection sampling
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Yang Song and Stefano Ermon · 2019
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Discriminator optimal transport
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Metropolis-Hastings generative adversarial networks
Ryan Turner, Jane Hung, Eric Frank, Yunus Saatchi, and Jason Yosinski · 2019
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A characteristic function approach to deep implicit generative modeling
Abdul Fatir Ansari, Jonathan Scarlett, and Harold Soh · 2020
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Your GAN is secretly an energy-based model and you should use discriminator driven latent sampling
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Residual energy-based models for text generation
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Improved techniques for training score-based generative models
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LOGAN: Latent optimisation for generative adversarial networks
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Generalized energy based models
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