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We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique to each.
Movement-produced stimulation in the development of visually guided behavior
Richard Held and Alan Hein · 1963
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Common information is far less than mutual information
Peter Gács and János Körner · 1973
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The information bottleneck method
Naftali Tishby, Fernando C. Pereira, and William Bialek · 1999
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Estimation of entropy and mutual information
Liam Paninski · 2003
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The im algorithm: A variational approach to information maximization
David Barber and Felix Agakov · 2003
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Zero-error information and applications in cryptography
Stefan Wolf and J Wultschleger · 2004
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Estimating mutual information
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger · 2004
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The development of embodied cognition: Six lessons from babies
Linda Smith and Michael Gasser · 2005
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Nonnegative decomposition of multivariate information
Paul L Williams and Randall D Beer · 2010
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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On deep multi-view representation learning
Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes · 2015
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Deep variational canonical correlation analysis
Weiran Wang, Xinchen Yan, Honglak Lee, and Karen Livescu · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Maximum entropy functions: Approximate gacs-korner for distributed compression
Salman Salamatian, Asaf Cohen, and Muriel Médard · 2016
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dsprites: Disentanglement testing sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
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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 · 2017
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Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
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3d shapes dataset
Chris Burgess and Hyunjik Kim · 2018
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Emergence of invariance and disentanglement in deep representations
Alessandro Achille and Stefano 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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Information dropout: Learning optimal representations through noisy computation
Practical lossless compression with latent variables using bits back coding
James Townsend, Thomas Bird, and David Barber · 2019
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Disentangling vae
Yann Dubois, Alexandros Kastanos, Dave Lines, and Bart Melman · 2019
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Approximate gács-körner common information
S. Salamatian, A. Cohen, and M. Médard · 2020
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Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
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Weakly-supervised disentanglement without compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen · 2020
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A theory of usable information under computational constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, and Stefano Ermon · 2020
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Alessandro Achille and Stefano Soatto · 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
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Ricky T. Q. Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
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On the information bottleneck theory of deep learning
Andrew Michael Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan Daniel Tracey, and David Daniel Cox · 2018
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Disentangled sequential autoencoder
Li Yingzhen and Stephan Mandt · 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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Multi-level variational autoencoder: Learning disentangled representations from grouped observations
Diane Bouchacourt, Ryota Tomioka, and Sebastian Nowozin · 2018
Cited alongside, same era.
What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Learning robust representations via multi-view information bottleneck
Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, and Zeynep Akata · 2020
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Learning optimal representations with the decodable information bottleneck
Yann Dubois, Douwe Kiela, David J Schwab, and Ramakrishna Vedantam · 2020
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Nestedvae: Isolating common factors via weak supervision
Matthew J Vowels, Necati Cihan Camgoz, and Richard Bowden · 2020
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Learning disentangled representations via mutual information estimation
Eduardo Hugo Sanchez, Mathieu Serrurier, and Mathias Ortner · 2020
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Usable information and evolution of optimal representations during training
Michael Kleinman, Alessandro Achille, Daksh Idnani, and Jonathan Kao · 2021
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Redundant information neural estimation
Michael Kleinman, Alessandro Achille, Stefano Soatto, and Jonathan C. Kao · 2021
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Rethinking minimal sufficient representation in contrastive learning
Haoqing Wang, Xun Guo, Zhi-Hong Deng, and Yan Lu · 2022
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A novel approach to the partial information decomposition
Artemy Kolchinsky · 2022
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