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Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models.
Solutions of ill-posed problems , volume 14
Andrey N Tikhonov and Vasilii Iakkovlevich Arsenin · 1977
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Creating artificial neural networks that generalize
Jocelyn Sietsma and Robert JF Dow · 1991
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The effects of adding noise during backpropagation training on a generalization performance
Guozhong An · 1996
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Markov chain Monte Carlo convergence diagnostics: a comparative review
Mary Kathryn Cowles and Bradley P Carlin · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Learning Multiple Layers of Features from Tiny Images, 2009
Alex Krizhevsky and Geoffrey Hinton · 2009
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The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray · 2011
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Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
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A generative process for sampling contractive auto-encoders
Salah Rifai, Yoshua Bengio, Yann Dauphin, and Pascal Vincent · 2012
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Generalized denoising auto-encoders as generative models
Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent · 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 Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
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Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep Learning Face Attributes in the Wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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What is high-throughput virtual screening? A perspective from organic materials discovery
Edward O Pyzer-Knapp, Changwon Suh, Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre, and Alán Aspuru-Guzik · 2015
Cited alongside, same era.
Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
Cited alongside, same era.
Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2016
Cited alongside, same era.
Elbo surgery: yet another way to carve up the variational evidence lower bound
Matthew D Hoffman and Matthew J Johnson · 2016
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Improving variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Cited alongside, same era.
Adversarial autoencoders
REBAR: low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker, Andriy Mnih, Chris J Maddison, John Lawson, and Jascha Sohl-Dickstein · 2017
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Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, et al · 2017
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Towards deeper understanding of variational autoencoding models
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
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Fixing a broken ELBO
Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif A Saurous, and Kevin Murphy · 2018
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Optimizing the latent space of generative networks
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2016
Cited alongside, same era.
A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2016
Cited alongside, same era.
Wide Residual Networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
End-to-end optimized image compression
Johannes Ballé, Valero Laparra, and Eero P Simoncelli · 2017
Cited alongside, same era.
Variational lossy autoencoder
Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2017
Cited alongside, same era.
Improved training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Cited alongside, same era.
Piotr Bojanowski, Armand Joulin, David Lopez-Paz, and Arthur Szlam · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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The GAN landscape: Losses, architectures, regularization, and normalization
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly · 2018
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Which training methods for GANs do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Danilo Jimenez Rezende and Fabio Viola · 2018
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Distribution matching in variational inference
Mihaela Rosca, Balaji Lakshminarayanan, and Shakir Mohamed · 2018
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Assessing generative models via precision and recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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VAE with a VampPrior
Jakub Tomczak and Max Welling · 2018
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Resampled priors for variational autoencoders
M. Bauer and A. Mnih · 2019
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Diagnosing and enhancing VAE models
Bin Dai and David Wipf · 2019
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Resisting adversarial attacks using Gaussian mixture variational autoencoders
Partha Ghosh, Arpan Losalka, and Michael J Black · 2019
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Generating diverse high-fidelity images with VQ-VAE-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2019
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