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We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities from one domain as entities from the other domain.
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Graphical Models, Exponential Families, and Variational Inference
Wainwright, Martin J. and Jordan, Michael I · 1935
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Variational learning in nonlinear Gaussian belief networks
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Adaptive proposal distribution for random walk Metropolis algorithm
Haario, Heikki, Saksman, Eero, and Tamminen, Johanna · 1999
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Variational learning in nonlinear Gaussian belief networks
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Adaptive proposal distribution for random walk Metropolis algorithm
Haario, Heikki, Saksman, Eero, and Tamminen, Johanna · 1999
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An Introduction to Variational Methods for Graphical Models
Jordan, Michael I., Ghahramani, Zoubin, Jaakkola, Tommi S., and Saul, Lawrence K · 1999
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Probabilistic Principal Component Analysis
Tipping, Michael E. and Bishop, Christopher M · 1999
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Bayesian non-linear independent component analysis by multi-layer perceptrons
Lappalainen, Harri and Honkela, Antti · 2000
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Bayesian non-linear independent component analysis by multi-layer perceptrons
Lappalainen, Harri and Honkela, Antti · 2000
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Gneiting, Tilmann and Raftery, Adrian E · 2007
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Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, Tilmann and Raftery, Adrian E · 2007
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Sugiyama, Masashi, Suzuki, Taiji, Nakajima, Shinichi, Kashima, Hisashi, von Bünau, Paul, and Kawanabe, Motoaki · 2008
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Direct importance estimation for covariate shift adaptation
Sugiyama, Masashi, Suzuki, Taiji, Nakajima, Shinichi, Kashima, Hisashi, von Bünau, Paul, and Kawanabe, Motoaki · 2008
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Nguyen, XuanLong, Wainwright, Martin J, and Jordan, Michael I · 2010
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Nguyen, XuanLong, Wainwright, Martin J, and Jordan, Michael I · 2010
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Bartholomew, David J, Knott, Martin, and Moustaki, Irini · 2011
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Latent variable models and factor analysis: A unified approach , volume 904
Bartholomew, David J, Knott, Martin, and Moustaki, Irini · 2011
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Density Ratio Estimation in Machine Learning
Sugiyama, Masashi, Suzuki, Taiji, and Kanamori, Takafumi · 2012
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Density Ratio Estimation in Machine Learning
Sugiyama, Masashi, Suzuki, Taiji, and Kanamori, Takafumi · 2012
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Amortized inference in probabilistic reasoning
Gershman, Samuel and Goodman, Noah · 2014
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Generative Adversarial Networks
Goodfellow, Ian J., Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
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f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
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Amortised map inference for image super-resolution
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Generative Adversarial Nets from a Density Ratio Estimation Perspective
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Variational Inference: A Review for Statisticians
Blei, David M, Kucukelbir, Alp, and McAuliffe, Jon D · 2017
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Auto-Encoding Variational Bayes
Kingma, Diederik P and Welling, Max · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan · 2014
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Amortized inference in probabilistic reasoning
Gershman, Samuel and Goodman, Noah · 2014
Cited alongside, same era.
Generative Adversarial Networks
Goodfellow, Ian J., Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
Cited alongside, same era.
Auto-Encoding Variational Bayes
Kingma, Diederik P and Welling, Max · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan · 2014
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Hu, Zhiting, Yang, Zichao, Salakhutdinov, Ruslan, and Xing, Eric P · 2017
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Variational Inference using Implicit Distributions
Huszár, Ferenc · 2017
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Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Kim, Taeksoo, Cha, Moonsu, Kim, Hyunsoo, Lee, Jung Kwon, and Kim, Jiwon · 2017
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Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
Mescheder, Lars, Nowozin, Sebastian, and Geiger, Andreas · 2017
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Learning in Implicit Generative Models
Mohamed, Shakir and Lakshminarayanan, Balaji · 2017
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Adversarial Symmetric Variational Autoencoder
Pu, Yuchen, Wang, Weiyao, Henao, Ricardo, Chen, Liqun, Gan, Zhe, Li, Chunyuan, and Carin, Lawrence · 2017
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Veegan: Reducing mode collapse in gans using implicit variational learning
Srivastava, Akash, Valkoz, Lazar, Russell, Chris, Gutmann, Michael U., Sutton, Charles, Valkov, Lazar, Russell, Chris, Gutmann, Michael U., and Sutton, Charles · 2017
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Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions
Titsias, Michalis K · 2017
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Hierarchical Implicit Models and Likelihood-Free Variational Inference
Tran, Dustin, Ranganath, Rajesh, and Blei, David · 2017
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Zhu, Jun-Yan, Park, Taesung, Isola, Phillip, and Efros, Alexei A · 2017
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Variational Inference: A Review for Statisticians
Blei, David M, Kucukelbir, Alp, and McAuliffe, Jon D · 2017
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On Unifying Deep Generative Models
Hu, Zhiting, Yang, Zichao, Salakhutdinov, Ruslan, and Xing, Eric P · 2017
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Variational Inference using Implicit Distributions
Huszár, Ferenc · 2017
Later among the works it cites.
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Kim, Taeksoo, Cha, Moonsu, Kim, Hyunsoo, Lee, Jung Kwon, and Kim, Jiwon · 2017
Later among the works it cites.
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
Mescheder, Lars, Nowozin, Sebastian, and Geiger, Andreas · 2017
Later among the works it cites.
Learning in Implicit Generative Models
Mohamed, Shakir and Lakshminarayanan, Balaji · 2017
Later among the works it cites.
Adversarial Symmetric Variational Autoencoder
Pu, Yuchen, Wang, Weiyao, Henao, Ricardo, Chen, Liqun, Gan, Zhe, Li, Chunyuan, and Carin, Lawrence · 2017
Later among the works it cites.
Veegan: Reducing mode collapse in gans using implicit variational learning
Srivastava, Akash, Valkoz, Lazar, Russell, Chris, Gutmann, Michael U., Sutton, Charles, Valkov, Lazar, Russell, Chris, Gutmann, Michael U., and Sutton, Charles · 2017
Later among the works it cites.
Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions
Titsias, Michalis K · 2017
Later among the works it cites.
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Tran, Dustin, Ranganath, Rajesh, and Blei, David · 2017
Later among the works it cites.
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Zhu, Jun-Yan, Park, Taesung, Isola, Phillip, and Efros, Alexei A · 2017
Later among the works it cites.