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Using powerful posterior distributions is a popular approach to achieving better variational inference.
“Approximation Capabilities of Multilayer Feedforward Networks”
Kurt Hornik · 1991
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“Calculus of Variations”
Izrail Gelfand and Richard. Silverman · 2000
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Ruslan Salakhutdinov and Iain Murray · 2008
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“The Neural Autoregressive Distribution Estimator”
Hugo Larochelle and Iain Murray · 2011
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“Density Ratio Estimation in Machine Learning”
Masashi Sugiyama, Taiji Suzuki and Takafumi Kanamori · 2012
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“Rectifier Nonlinearities Improve Neural Network Acoustic Models”
Andrew. Maas, Awni. Hannun and Andrew. Ng · 2013
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“Auto-Encoding Variational Bayes”
Diederik Kingma and Max Welling · 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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“Importance Weighted Autoencoders”
Yuri Burda, Roger Grosse and Ruslan Salakhutdinov · 2015
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“Adam: A Method for Stochastic Optimization”
Diederik. Kingma and Jimmy Ba · 2015
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“Human-Level Concept Learning through Probabilistic Program Induction”
B.. Lake, R. Salakhutdinov and J.. Tenenbaum · 2015
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“Variational Inference with Normalizing Flows”
Danilo Rezende and Shakir Mohamed · 2015
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“Markov Chain Monte Carlo and Variational Inference: Bridging the Gap”
Tim Salimans, Diederik Kingma and Max Welling · 2015
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“Generating Sentences from a Continuous Space.”
Samuel Bowman, Luke Vilnis, Oriol Vinyals, Andrew. Dai, Rafal Jozefowicz and Samy Bengio · 2016
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“Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders”
Nat Dilokthanakul, Pedro Mediano, Marta Garnelo, Matthew Lee, Hugh Salimbeni, Kai Arulkumaran and Murray Shanahan · 2016
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“Density Estimation Using Real NVP”
Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio · 2016
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“Elbo Surgery: Yet Another Way to Carve up the Variational Evidence Lower Bound”
Matthew. Hoffman and Matthew. Johnson · 2016
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“Improved Variational Inference with Inverse Autoregressive Flow”
Diederik. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever and Max Welling · 2016
Cited alongside, same era.
“Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks”
Lars Mescheder, Sebastian Nowozin and Andreas Geiger · 2017
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“Stick-Breaking Variational Autoencoders”
Eric. Nalisnick and Padhraic Smyth · 2017
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“PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications”
Tim Salimans, Andrej Karpathy, Xi Chen and Diederik. Kingma · 2017
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“Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms”
Han Xiao, Kashif Rasul and Roland Vollgraf · 2017
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“Resampled Priors for Variational Autoencoders”
Matthias Bauer and Andriy Mnih · 2018
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“Approximate Inference for Deep Latent Gaussian Mixtures”
Eric Nalisnick, Lars Hertel and Padhraic Smyth · 2016
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“Ladder Variational Autoencoders”
Casper Snderby, Tapani Raiko, Lars Maale, Sren Snderby and Ole Winther · 2016
Cited alongside, same era.
“Variational Gaussian Process”
Dustin Tran, Rajesh Ranganath and David. Blei · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
“Variational Lossy Autoencoder”
Xi Chen, Diederik. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever and Pieter Abbeel · 2017
Cited alongside, same era.
“Nonparametric Variational Auto-Encoders for Hierarchical Representation Learning”
Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang and Eric. Xing · 2017
Cited alongside, same era.
“PixelVAE: A Latent Variable Model for Natural Images”
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ta\"ga, Francesco Visin, David V\’azquez and Aaron. Courville · 2017
Cited alongside, same era.
“Sylvester Normalizing Flows for Variational Inference”
Rianne van Berg, Leonard Hasenclever, Jakub. Tomczak and Max Welling · 2018
Later among the works it cites.
“Glow: Generative Flow with Invertible 1x1 Convolutions”
Durk Kingma and Prafulla Dhariwal · 2018
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Danilo 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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“Variational Autoencoder with Implicit Optimal Priors”
Hiroshi Takahashi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada and Satoshi Yagi · 2018
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“VAE with a VampPrior”
Jakub Tomczak and Max Welling · 2018
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“BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling”
Lars Maale, Marco Fraccaro, Valentin Li\’evin and Ole Winther · 2019
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