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Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks.
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Cogitas, ergo sum. the role of data protection law and non-discrimination law in group profiling in the private sector
Wim Schreurs, Mireille Hildebrandt, Els Kindt, and Michaël Vanfleteren · 2008
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Measuring invariances in deep networks
Ian Goodfellow, Honglak Lee, Quoc V Le, Andrew Saxe, and Andrew Y Ng · 2009
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Causality
Judea Pearl · 2009
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Transforming auto-encoders
Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang · 2011
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Scikit-learn: Machine learning in Python
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Why unbiased computational processes can lead to discriminative decision procedures
Toon Calders and Indrė Žliobaitė · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Discovering hidden factors of variation in deep networks
Brian Cheung, Jesse A Livezey, Arjun K Bansal, and Bruno A Olshausen · 2014
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Learning the irreducible representations of commutative lie groups
Taco Cohen and Max Welling · 2014
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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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Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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Big data: Seizing opportunities, preserving values. executive office of the president. washington, dc: The white house, 2014
John Podesta, Penny Pritzker, Ernest J Moniz, John Holdren, and Jeffrey Zients · 2014
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Learning to disentangle factors of variation with manifold interaction
Scott Reed, Kihyuk Sohn, Yuting Zhang, and Honglak Lee · 2014
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Censoring representations with an adversary
Harrison Edwards and Amos Storkey · 2015
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Learning to linearize under uncertainty
Ross Goroshin, Michael F Mathieu, and Yann LeCun · 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
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Understanding image representations by measuring their equivariance and equivalence
Karel Lenc and Andrea Vedaldi · 2015
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2015
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Deep visual analogy-making
Scott Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 2015
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Weakly-supervised disentangling with recurrent transformations for 3D view synthesis
Jimei Yang, Scott E Reed, Ming-Hsuan Yang, and Honglak Lee · 2015
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On the relation between accuracy and fairness in binary classification
Indre Zliobaite · 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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Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
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A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher KI Williams · 2018
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Towards a definition of disentangled representations
Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, and Alexander Lerchner · 2018
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Scan: Learning hierarchical compositional visual concepts
Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P Burgess, Matko Bošnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, and Alexander Lerchner · 2018
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Learning to decompose and disentangle representations for video prediction
Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F Fei-Fei, and Juan Carlos Niebles · 2018
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Big data’s disparate impact
Solon Barocas and Andrew D Selbst · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 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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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
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Unsupervised feature extraction by time-contrastive learning and nonlinear ica
Aapo Hyvarinen and Hiroshi Morioka · 2016
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Learning representations for counterfactual inference
Fredrik Johansson, Uri Shalit, and David Sontag · 2016
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Disentangling factors of variation in deep representation using adversarial training
Michael F Mathieu, Junbo J Zhao, Aditya Ramesh, Pablo Sprechmann, and Yann LeCun · 2016
Cited alongside, same era.
Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 2018
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Learning latent subspaces in variational autoencoders
Jack Klys, Jake Snell, and Richard Zemel · 2018
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Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2018
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Curiosity driven exploration of learned disentangled goal spaces
Adrien Laversanne-Finot, Alexandre Pere, and Pierre-Yves Oudeyer · 2018
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Competitive training of mixtures of independent deep generative models
Francesco Locatello, Damien Vincent, Ilya Tolstikhin, Gunnar Rätsch, Sylvain Gelly, and Bernhard Schölkopf · 2018
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Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
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Disentangling disentanglement in variational auto-encoders
Emile Mathieu, Tom Rainforth, N. Siddharth, and Yee Whye Teh · 2018
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Learning deep disentangled embeddings with the f-statistic loss
Karl Ridgeway and Michael C Mozer · 2018
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Learning controllable fair representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, and Stefano Ermon · 2018
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Improving generalization for abstract reasoning tasks using disentangled feature representations
Xander Steenbrugge, Sam Leroux, Tim Verbelen, and Bart Dhoedt · 2018
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Recent advances in autoencoder-based representation learning
Michael Tschannen, Olivier Bachem, and Mario Lucic · 2018
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Disentangled sequential autoencoder
Li Yingzhen and Stephan Mandt · 2018
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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
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Fairness and machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
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Structured disentangled representations
Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N Siddharth, Brooks Paige, Dana H Brooks, Jennifer Dy, and Jan-Willem Meent · 2019
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Deep self-organization: Interpretable discrete representation learning on time series
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, and Gunnar Rätsch · 2019
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On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Muhammad Waleed Gondal, Manuel Wüthrich, Djordje Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 2019
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The incomplete rosetta stone problem: Identifiability results for multi-view nonlinear ica
Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, and Bernhard Schölkopf · 2019
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Nonlinear ica using auxiliary variables and generalized contrastive learning
Aapo Hyvarinen, Hiroaki Sasaki, and Richard E Turner · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A Alemi, and George Tucker · 2019
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Variational autoencoders recover pca directions (by accident)
Michal Rolinek, Dominik Zietlow, and Georg Martius · 2019
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Learning disentangled representations with reference-based variational autoencoders
Adrià Ruiz, Oriol Martinez, Xavier Binefa, and Jakob Verbeek · 2019
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ISA-VAE: Independent subspace analysis with variational autoencoders, 2019
Jan Stühmer, Richard Turner, and Sebastian Nowozin · 2019
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Interventional robustness of deep latent variable models
Raphael Suter, Djordje Miladinović, Stefan Bauer, and Bernhard Schölkopf · 2019
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Are disentangled representations helpful for abstract visual reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem · 2019
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