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The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling.
Calculating the singular values and pseudo-inverse of a matrix
G. H. Golub and W. Kahan · 1965
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Auto-association by multilayer perceptrons and singular value decomposition
H. Bourlard and Y. Kamp · 1987
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Eigenfaces for recognition
Matthew Turk and Alex Pentland · 1991
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Learning factorial codes by predictability minimization
Jürgen Schmidhuber · 1992
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Numerical linear algebra
Lloyd N Trefethen and David Bau III · 1997
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Disentangling factors of variation via generative entangling
Guillaume Desjardins, Aaron Courville, and Yoshua Bengio · 2012
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Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. 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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An Introduction to Statistical Learning: With Applications in R
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani · 2014
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Auto-Encoding Variational Bayes
D. P Kingma and M. 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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Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra · 2015
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Deep convolutional inverse graphics network
Tejas D Kulkarni, William F. Whitney, Pushmeet Kohli, and Josh Tenenbaum · 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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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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Modeling and transforming speech using variational autoencoders
Merlijn Blaauw and Jordi Bonada · 2016
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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Towards conceptual compression
K. Gregor, F. Besse, D. Jimenez Rezende, I. Danihelka, and D. Wierstra · 2016
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Improved variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Improved variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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A deep reinforcement learning chatbot
Iulian Vlad Serban, Chinnadhurai Sankar, Mathieu Germain, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Mudumba, Alexandre de Brébisson, Jose Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, and Yoshua Bengio · 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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Improved variational autoencoders for text modeling using dilated convolutions
Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick · 2017
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Advances in variational inference
Cheng Zhang, Judith Bütepage, Hedvig Kjellström, and Stephan Mandt · 2017
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Fixing a broken ELBO
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Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, and Daan Wierstra · 2016
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A survey of inductive biases for factorial representation-learning
Karl Ridgeway · 2016
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Stable reinforcement learning with autoencoders for tactile and visual data
H. van Hoof, N. Chen, M. Karl, P. van der Smagt, and J. Peters · 2016
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Deep predictive policy training using reinforcement learning
A. Ghadirzadeh, A. Maki, D. Kragic, and M. Björkman · 2017
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Beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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DARLA: Improving zero-shot transfer in reinforcement learning
I. Higgins, A. Pal, A. A. Rusu, L. Matthey, C. P Burgess, A. Pritzel, M. Botvinick, C. Blundell, and A. Lerchner · 2017
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Deep feature consistent variational autoencoder
X. Hou, L. Shen, K. Sun, and G. Qiu · 2017
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Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif A. Saurous, and Kevin Murphy · 2018
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Understanding disentangling in
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Isolating sources of disentanglement in variational autoencoders
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Hidden talents of the variational autoencoder
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Disentangling disentanglement in variational auto-encoders
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Visual Reinforcement Learning with Imagined Goals
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Diagnosing and enhancing vae models
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From variational to deterministic autoencoders
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ISA-VAE: Independent subspace analysis with variational autoencoders
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