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Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks.
Learning factorial codes by predictability minimization
Jürgen Schmidhuber · 1992
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Independent component analysis, a new concept?
Pierre Comon · 1994
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Canonical correlation analysis; an overview with application to learning methods
David R. Hardoon, Sandor Szedmak, and John Shawe-Taylor · 2004
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Representational similarity analysis – connecting the branches of systems neuroscience
Nikolaus Kriegeskorte, Marieke Mur, and Peter Bandettini · 2008
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Steps toward a theory of visual information
Stefano Soatto · 2010
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Algorithms for hyper-parameter optimization
James Bergstra, Remi Bardenet, Yoshua Bengio, and Balazs Kegl · 2011
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger Hoos, and Kevin Leyton-Brown · 2011
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Practical Bayesian Optimization of Machine Learning Algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
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Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
C. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-Brown · 2012
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Deep symmetry networks
Robert Gens and Pedro M. Domingos · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 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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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Towards deep symbolic reinforcement learning
Marta Garnelo, Kai Arulkumaran, and Murray Shanahan · 2016
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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 · 2016
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Convergent learning: Do different neural networks learn the same representations?
Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John Hopcroft · 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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Understanding disentangling in β \beta -VAE
Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2017
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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 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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Curiosity driven exploration of learned disentangled goal spaces
Adrien Laversanne-Finot, Alexandre Péré, and Pierre-Yves Oudeyer · 2018
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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 · 2018
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Deep learning: A critical appraisal
Gary Marcus · 2018
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Insights on representational similarity in neural networks with canonical correlation
Ari S. Morcos, Maithra Raghu, and Samy Bengio · 2018
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Visual reinforcement learning with imagined goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Generalized elbo with constrained optimization, geco
Danilo J Rezende and Fabio Viola · 2018
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dsprites: Disentanglement testing sprites dataset, 2017
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
Cited alongside, same era.
Evolving Deep Neural Networks
Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, and Babak Hodjat · 2017
Cited alongside, same era.
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
Life-long disentangled representation learning with cross-domain latent homologies
Alessandro Achille, Tom Eccles, Loic Matthey, Christopher P Burgess, Nick Watters, Alexander Lerchner, and Irina Higgins · 2018
Cited alongside, same era.
3d shapes dataset
Chris Burgess and Hyunjik Kim · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
Cited alongside, same era.
A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher K. I. Williams · 2018
Cited alongside, same era.
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Learning deep disentangled embeddings with the f-statistic loss
Karl Ridgeway and Michael C Mozer · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis · 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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Interventional robustness of deep latent variable models
Raphael Suter, Dorde Miladinovic, Stefan Bauer, and Bernhard Scholkopf · 2018
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Towards understanding learning representations: To what extent do different neural networks learn the same representation
Liwei Wang, Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Kun He, and John Hopcroft · 2018
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MONet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nick Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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On the fairness of disentangled representations
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, and Olivier Bachem · 2019
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Disentangling disentanglement in variational autoencoders
Emile Mathieu, Tom Rainforth, N. Siddharth, and Yee Whye Teh · 2019
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Variational autoencoders pursue pca directions (by accident)
Michal Rolinek, Dominik Zietlow, and Georg Martius · 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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Nicholas Watters, Loic Matthey, Matko Bosnjak, Christopher P. Burgess, and Alexander Lerchner · 2019
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