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Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner.
Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, 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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A neural algorithm of artistic style
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2015
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Bayesian representation learning with oracle constraints
Theofanis Karaletsos, Serge Belongie, and Gunnar Rätsch · 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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Deep Variational Information Bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy · 2016
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2016
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2016
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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 · 2016
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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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Neural variational inference for text processing
Yishu Miao, Lei Yu, and Phil Blunsom · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Disentangling Nonlinear Perceptual Embeddings With Multi-Query Triplet Networks
Andreas Veit, Serge Belongie, and Theofanis Karaletsos · 2016
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Multi-level variational autoencoder: Learning disentangled representations from grouped observations
Diane Bouchacourt, Ryota Tomioka, and Sebastian Nowozin · 2017
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Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models
Variational autoencoder for semi-supervised text classification
Weidi Xu, Haoze Sun, Chao Deng, and Ying Tan · 2017
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InfoVAE: Information maximizing variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
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Information Dropout: Learning Optimal Representations Through Noisy Computation
A. Achille and S. Soatto · 2018
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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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Understanding disentangling in β \beta -vae
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
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Jesse Engel, Matthew Hoffman, and Adam Roberts · 2017
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PixelVAE: A latent variable model for natural images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville · 2017
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Categorical reparameterization with Gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2017
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Learning disentangled representations with semi-supervised deep generative models
N Siddharth, Brooks Paige, Jan-Willem Van de Meent, Alban Desmaison, Frank Wood, Noah D Goodman, Pushmeet Kohli, and Philip HS Torr · 2017
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Tian Qi Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
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Joint-vae: Learning disentangled joint continuous and discrete representations
Emilien Dupont · 2018
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A Framework for the Quantitative Evaluation of Disentangled Representations
Cian Eastwood and Christopher K. I. Williams · 2018
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Auto-encoding total correlation explanation
Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, and Aram Galstyan · 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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Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
Sarthak Jain, Edward Banner, Jan-Willem van de Meent, Iain J. Marshall, and Byron C. Wallace · 2018
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Hyunjik Kim and Andriy Mnih · 2018
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20 newsgroups data set, 2007
Ken Lang · 2018
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Variational Autoencoders for Collaborative Filtering
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara · 2018
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