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Advances in unsupervised learning enable reconstruction and generation of samples from complex distributions, but this success is marred by the inscrutability of the representations learned.
Information theoretical analysis of multivariate correlation
Watanabe, Satosi · 1960
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Self-organization in a perceptual network
Linsker, Ralph · 1988
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Unsupervised learning
Barlow, Horace · 1989
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Learning factorial codes by predictability minimization
Schmidhuber, Jürgen · 1992
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Independent component analysis, a new concept?
Comon, Pierre · 1994
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The multiinformation function as a tool for measuring stochastic dependence
Studenỳ, M and Vejnarova, J · 1998
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The information bottleneck method
Tishby, Naftali, Pereira, Fernando C, and Bialek, William · 2000
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The im algorithm: a variational approach to information maximization
Barber, David and Agakov, Felix · 2003
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Elements of information theory
Cover, Thomas M and Thomas, Joy A · 2006
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Representation learning: A review and new perspectives
Bengio, Yoshua, Courville, Aaron, and Vincent, Pascal · 2013
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Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2013
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Nice: Non-linear independent components estimation
Dinh, Laurent, Krueger, David, and Bengio, Yoshua · 2014
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Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 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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Discovering structure in high-dimensional data through correlation explanation
Ver Steeg, Greg and Galstyan, Aram · 2014
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Maximally informative hierarchical representations of high-dimensional data
Ver Steeg, Greg and Galstyan, Aram · 2015
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Independently controllable features
Bengio, Emmanuel, Thomas, Valentin, Pineau, Joelle, Precup, Doina, and Bengio, Yoshua · 2017
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Pixelvae: A latent variable model for natural images
Gulrajani, Ishaan, Kumar, Kundan, Ahmed, Faruk, Taiga, Adrien Ali, Visin, Francesco, Vazquez, David, and Courville, Aaron · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, Irina, Matthey, Loic, Pal, Arka, Burgess, Christopher, Glorot, Xavier, Botvinick, Matthew, Mohamed, Shakir, and Lerchner, Alexander · 2017
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Disentangling by factorising
Kim, Hyunjik and Mnih, Andriy · 2017
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Opening the black box of deep neural networks via information
Shwartz-Ziv, Ravid and Tishby, Naftali · 2017
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Chen, Xi, Duan, Yan, Houthooft, Rein, Schulman, John, Sutskever, Ilya, and Abbeel, Pieter · 2016
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Deep unsupervised clustering with gaussian mixture variational autoencoders
Dilokthanakul, Nat, Mediano, Pedro AM, Garnelo, Marta, Lee, Matthew CH, Salimbeni, Hugh, Arulkumaran, Kai, and Shanahan, Murray · 2016
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Ladder variational autoencoders
Sønderby, Casper Kaae, Raiko, Tapani, Maaløe, Lars, Sønderby, Søren Kaae, and Winther, Ole · 2016
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Pixel recurrent neural networks
Van Oord, Aaron, Kalchbrenner, Nal, and Kavukcuoglu, Koray · 2016
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On the emergence of invariance and disentangling in deep representations
Achille, Alessandro and Soatto, Stefano · 2017
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Deep variational information bottleneck
Alemi, Alexander A, Fischer, Ian, Dillon, Joshua V, and Murphy, Kevin · 2017
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Infovae: Information maximizing variational autoencoders
Zhao, Shengjia, Song, Jiaming, and Ermon, Stefano
Cited in the paper.
Independently controllable features
Thomas, Valentin, Pondard, Jules, Bengio, Emmanuel, Sarfati, Marc, Beaudoin, Philippe, Meurs, Marie-Jean, Pineau, Joelle, Precup, Doina, and Bengio, Yoshua · 2017
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Unsupervised learning via total correlation explanation
Ver Steeg, Greg · 2017
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Low complexity gaussian latent factor models and a blessing of dimensionality
Ver Steeg, Greg and Galstyan, Aram · 2017
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Information dropout: Learning optimal representations through noisy computation
Achille, Alessandro and Soatto, Stefano · 2018
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On the information bottleneck theory of deep learning
Saxe, Michael A, Bansal, Yamini, Dapello, Joel, Advani, Madhu, Kolchinsky, Artemy, Daniel, Brendan T, and Cox, David D · 2018
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