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Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models.
“Grammar variational autoencoder”
Matt Kusner, Brooks Paige and Jos\’e Hern\’andez-Lobato · 1954
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“Maximum likelihood from incomplete data via the EM algorithm”
Arthur Dempster, Nan Laird and Donald Rubin · 1977
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“Auto-association by multilayer perceptrons and singular value decomposition”
Herv\’e Bourlard and Yves Kamp · 1988
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“Learning representations by back-propagating errors”
David Rumelhart, Geoffrey Hinton and Ronald Williams · 1988
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“An Application of the Principle of Maximum Information Preservation to Linear Systems”
Ralph Linsker · 1989
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“Likelihood ratio gradient estimation for stochastic systems”
Peter Glynn · 1990
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“Simple statistical gradient-following algorithms for connectionist reinforcement learning”
Ronald Williams · 1992
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“The Helmholtz machine”
Peter Dayan, Geoffrey Hinton, Radford Neal and Richard Zemel · 1995
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“Higher order statistical decorrelation without information loss”
Gustavo Deco and Wilfried Brauer · 1995
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“The "Wake-Sleep" algorithm for unsupervised neural networks”
Geoffrey Hinton, Peter Dayan, Brendan Frey and Radford Neal · 1995
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“Optimization and sensitivity analysis of computer simulation models by the score function method”
Jack Kleijnen and Reuven Rubinstein · 1996
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“Long Short-Term Memory”
Sepp Hochreiter and J\"urgen Schmidhuber · 1997
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“Gradient-based learning applied to document recognition”
Yann LeCun, L\’eon Bottou, Yoshua Bengio and Patrick Haffner · 1998
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“A view of the EM algorithm that justifies incremental, sparse, and other variants”
Radford Neal and Geoffrey Hinton · 1998
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“EM algorithms for PCA and SPCA”
Sam Roweis · 1998
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“Gradient estimation”
Michael Fu · 2006
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“An analysis of logistic models: Exponential family connections and online performance”
Arindam Banerjee · 2007
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“Fast inference in sparse coding algorithms with applications to object recognition”, 2008
Koray Kavukcuoglu, Marc’Aurelio Ranzato and Yann LeCun · 2008
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“Graphical models, exponential families, and variational inference”
Martin Wainwright and Michael Jordan · 2008
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“Probabilistic graphical models: Principles and techniques”
Daphne Koller and Nir Friedman · 2009
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“Efficient learning of deep Boltzmann machines”
Ruslan Salakhutdinov and Hugo Larochelle · 2010
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“Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion”
Pascal Vincent et al · 2010
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“Practical variational inference for neural networks”
Alex Graves · 2011
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“MCMC Using Hamiltonian Dynamics”
R Neal · 2011
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“Variational Bayesian inference with stochastic search”
David Blei, Michael Jordan and John Paisley · 2012
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“Variational Bayesian Inference with stochastic search”
John Paisley, David Blei and Michael Jordan · 2012
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“Representation Learning: A Review and New Perspectives”
Yoshua Bengio, Aaron Courville and Pascal Vincent · 2013
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“Monte Carlo methods in financial engineering”
Paul Glasserman · 2013
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“Stochastic variational inference”
Matthew Hoffman, David Blei, Chong Wang and John Paisley · 2013
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“Fixed-Form variational posterior approximation through stochastic linear regression”
Tim Salimans and David Knowles · 2013
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“Automated variational inference in probabilistic programming”
David Wingate and Theophane Weber · 2013
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“Learning stochastic recurrent networks”
Justin Bayer and Christian Osendorfer · 2014
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“Deep generative stochastic networks trainable by backprop”
Yoshua Bengio, Eric Laufer, Guillaume Alain and Jason Yosinski · 2014
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“NICE: Non-linear independent components estimation”
Laurent Dinh, David Krueger and Yoshua Bengio · 2014
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“Amortized inference in probabilistic reasoning.”
Samuel Gershman and Noah Goodman · 2014
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“Generative adversarial nets”
Ian Goodfellow et al · 2014
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“Deep AutoRegressive Networks”
Karol Gregor et al · 2014
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“Semi-supervised learning with deep generative models”
Diederik Kingma, Shakir Mohamed, Danilo Rezende and Max Welling · 2014
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“Auto-Encoding Variational Bayes”
Diederik Kingma and Max Welling · 2014
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“Doubly stochastic variational Bayes for non-conjugate inference”, 2014
Miguel L\’azaro-Gredilla · 2014
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“Neural variational inference and learning in belief networks”
Andriy Mnih and Karol Gregor · 2014
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“Black Box Variational Inference”
Rajesh Ranganath, Sean Gerrish and David Blei · 2014
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“Stochastic backpropagation and approximate inference in deep generative models”
Danilo Rezende, Shakir Mohamed and Daan Wierstra · 2014
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“Weight Uncertainty in Neural Networks”
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu and Daan Wierstra · 2015
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“Generating sentences from a continuous space”
Samuel Bowman et al · 2015
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“Importance weighted autoencoders”
Yuri Burda, Roger Grosse and Ruslan Salakhutdinov · 2015
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“A recurrent latent variable model for sequential data”
Junyoung Chung et al · 2015
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“Learning to generate chairs with convolutional neural networks”
Alexey Dosovitskiy, Jost Tobias and Thomas Brox · 2015
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“Fast second order stochastic backpropagation for variational inference”
Kai Fan et al · 2015
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“Made: Masked autoencoder for distribution estimation”
Mathieu Germain, Karol Gregor, Iain Murray and Hugo Larochelle · 2015
Cited alongside, same era.
“DRAW: A Recurrent Neural Network For Image Generation”
Karol Gregor et al · 2015
Cited alongside, same era.
“MuProp: Unbiased backpropagation for stochastic neural networks”
Shixiang Gu, Sergey Levine, Ilya Sutskever and Andriy Mnih · 2015
Cited alongside, same era.
“Delving deep into rectifiers: Surpassing human-level performance on imagenet classification”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2015
Cited alongside, same era.
“Learning continuous control policies by stochastic value gradients”
“Unsupervised learning of 3d structure from images”
Danilo Rezende et al · 2016
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“The generalized reparameterization gradient”
Francisco Ruiz, Michalis AUEB and David Blei · 2016
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“A structured variational auto-encoder for learning deep hierarchies of sparse features”
Tim Salimans · 2016
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“How to train deep variational autoencoders and probabilistic ladder networks”
Casper Snderby et al · 2016
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“Ladder variational autoencoders”
Casper Snderby et al · 2016
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“Improving variational auto-encoders using householder flow”
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Nicolas Heess et al · 2015
Cited alongside, same era.
“An empirical exploration of recurrent network architectures”
Rafal Jozefowicz, Wojciech Zaremba and Ilya Sutskever · 2015
Cited alongside, same era.
“Variational dropout and the local reparameterization trick”
Diederik Kingma, Tim Salimans and Max Welling · 2015
Cited alongside, same era.
“Adam: A Method for Stochastic Optimization”
Diederik Kingma and Jimmy Ba · 2015
Cited alongside, same era.
“Deep convolutional inverse graphics network”
Tejas Kulkarni, William Whitney, Pushmeet Kohli and Josh Tenenbaum · 2015
Cited alongside, same era.
“Deep Learning”
Yann LeCun, Yoshua Bengio and Geoffrey Hinton · 2015
Cited alongside, same era.
“Generative moment matching networks”
Yujia Li, Kevin Swersky and Richard Zemel · 2015
Cited alongside, same era.
Jakub Tomczak and Max Welling · 2016
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“Conditional image generation with PixelCNN decoders”
Aaron Van et al · 2016
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“Pixel Recurrent Neural Networks”
Aaron Van, Nal Kalchbrenner and Koray Kavukcuoglu · 2016
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“Sampling Generative Networks: Notes on a Few Effective Techniques”
Tom White · 2016
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“Attribute2image: Conditional image generation from visual attributes”
Xinchen Yan, Jimei Yang, Kihyuk Sohn and Honglak Lee · 2016
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“Sylvester Normalizing Flows for Variational Inference”
Rianne Berg, Leonard Hasenclever, Jakub Tomczak and Max Welling · 2017
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“Neural photo editing with introspective adversarial networks”
Andrew Brock, Theodore Lim, James Ritchie and Nicholas Weston · 2017
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“Variational lossy autoencoder”
Xi Chen et al · 2017
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“Re-interpreting importance weighted autoencoders”
Chris Cremer, Quaid Morris and David Duvenaud · 2017
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“Learning diverse image colorization”
Aditya Deshpande et al · 2017
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“Adversarially learned inference”
Vincent Dumoulin et al · 2017
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“Towards a neural statistician”
Harrison Edwards and Amos Storkey · 2017
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“Bayesian recurrent neural networks”
Meire Fortunato, Charles Blundell and Oriol Vinyals · 2017
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“PixelVAE: A latent variable model for natural images”
Ishaan Gulrajani et al · 2017
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“beta-vae: Learning basic visual concepts with a constrained variational framework”
Irina Higgins et al · 2017
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Chin-Cheng Hsu et al · 2017
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“Controllable text generation”
Zhiting Hu et al · 2017
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“Categorical Reparameterization with Gumbel-Softmax”
Eric Jang, Shixiang Gu and Ben Poole · 2017
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“Deep variational bayes filters: Unsupervised learning of state space models from raw data”
Maximilian Karl, Maximilian Soelch, Justin Bayer and Patrick van Smagt · 2017
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“Structured Inference Networks for Nonlinear State Space Models.”
Rahul Krishnan, Uri Shalit and David Sontag · 2017
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“Automatic differentiation variational inference”
Alp Kucukelbir et al · 2017
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“Bayesian compression for deep learning”
Christos Louizos, Karen Ullrich and Max Welling · 2017
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“Multiplicative normalizing flows for variational Bayesian neural networks”
Christos Louizos and Max Welling · 2017
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“The concrete distribution: A continuous relaxation of discrete random variables”
Chris Maddison, Andriy Mnih and Yee Teh · 2017
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“Variational dropout sparsifies deep neural networks”
Dmitry Molchanov, Arsenii Ashukha and Dmitry Vetrov · 2017
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“Reparameterization gradients through acceptance-rejection sampling algorithms”
Christian Naesseth, Francisco Ruiz, Scott Linderman and David Blei · 2017
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“Masked autoregressive flow for density estimation”
George Papamakarios, Iain Murray and Theo Pavlakou · 2017
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“Neural episodic control”
Alexander Pritzel et al · 2017
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“Enabling dark energy science with deep generative models of galaxy images”
Siamak Ravanbakhsh et al · 2017
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“Sticking the landing: Simple, lower-variance gradient estimators for variational inference”
Geoffrey Roeder, Yuhuai Wu and David Duvenaud · 2017
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“A hybrid convolutional variational autoencoder for text generation”
Stanislau Semeniuta, Aliaksei Severyn and Erhardt Barth · 2017
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“A hierarchical latent variable encoder-decoder model for generating dialogues”
Iulian Serban et al · 2017
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“Improving variational auto-encoders using convex combination linear inverse autoregressive flow”
Jakub Tomczak and Max Welling · 2017
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“Deep probabilistic programming”
Dustin Tran et al · 2017
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“Variational autoencoder for semi-supervised text classification”
Weidi Xu, Haoze Sun, Chao Deng and Ying Tan · 2017
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“Improved variational autoencoders for text modeling using dilated convolutions”
Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov and Taylor Berg-Kirkpatrick · 2017
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“Learning discourse-level diversity for neural dialog models using conditional variational autoencoders”
Tiancheng Zhao, Ran Zhao and Maxine Eskenazi · 2017
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“Isolating sources of disentanglement in VAEs”
Ricky Chen, Xuechen Li, Roger Grosse and David Duvenaud · 2018
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“Automatic chemical design using a data-driven continuous representation of molecules”
Rafael G\’omez-Bombarelli et al · 2018
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“Flow-GAN: Combining maximum likelihood and adversarial learning in generative models”
Aditya Grover, Manik Dhar and Stefano Ermon · 2018
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“Distribution matching in variational inference”
Mihaela Rosca, Balaji Lakshminarayanan and Shakir Mohamed · 2018
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“Neural Autoregressive Flows”
Chin-Wei Huang, David Krueger, Alexandre Lacoste and Aaron Courville · 2092
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