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The information bottleneck (IB) method is a technique for extracting information that is relevant for predicting the target random variable from the source random variable, which is typically implemented by optimizing the IB Lagrangian that balances the compression and prediction terms.
Source coding with side information and a converse for degraded broadcast channels
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A conditional entropy bound for a pair of discrete random variables
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Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
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The information bottleneck method
Tishby, N.; Pereira, F. C.; and Bialek, W. 2000 · 2000
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Neural coding and decoding: communication channels and quantization
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Friedman, J.; Hastie, T.; and Tibshirani, R. 2001 · 2001
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Information Loss in an Optimal Maximum Likelihood Decoding
Samengo, I. 2002 · 2002
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An Information Theoretic Tradeoff between Complexity and Accuracy
Gilad-Bachrach, R.; Navot, A.; and Tishby, N. 2003 · 2003
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Information theory, inference and learning algorithms
MacKay, D. J.; and Mac Kay, D. J. 2003 · 2003
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Information bottleneck for Gaussian variables
Chechik, G.; Globerson, A.; Tishby, N.; and Weiss, Y. 2005 · 2005
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Information-based clustering
Slonim, N.; Atwal, G. S.; Tkačik, G.; and Bialek, W. 2005 · 2005
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Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization
Nguyen, X.; Wainwright, M. J.; and Jordan, M. I. 2008 · 2008
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Design of network coding functions in multihop relay networks
Zeitler, G.; Koetter, R.; Bauch, G.; and Widmer, J. 2008 · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Speaker recognition by Gaussian information bottleneck
Hecht, R. M.; Noor, E.; and Tishby, N. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Learning and generalization with the information bottleneck
Shamir, O.; Sabato, S.; and Tishby, N. 2010 · 2010
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Elements of information theory
Cover, T. M.; and Thomas, J. A. 2012 · 2012
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Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation
Sugiyama, M.; Suzuki, T.; and Kanamori, T. 2012 · 2012
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Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
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Intriguing properties of neural networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I. J.; and Fergus, R. 2014 · 2014
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Explaining and Harnessing Adversarial Examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015 · 2015
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Han, S.; Mao, H.; and Dally, W. J. 2015 · 2015
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Deep visual analogy-making
Reed, S. E.; Zhang, Y.; Zhang, Y.; and Lee, H. 2015 · 2015
Cited alongside, same era.
Sun RGB-D: A RGB-D scene understanding benchmark suite
Song, S.; Lichtenberg, S. P.; and Xiao, J. 2015 · 2015
Cited alongside, same era.
Deep learning and the information bottleneck principle
Tishby, N.; and Zaslavsky, N. 2015 · 2015
Cited alongside, same era.
Relevant sparse codes with variational information bottleneck
Chalk, M.; Marre, O.; and Tkacik, G. 2016 · 2016
A two-step disentanglement method
Hadad, N.; Wolf, L.; and Shahar, M. 2018 · 2018
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On the equivalence of double maxima and KL-means for information bottleneck-based source coding
Hassanpour, S.; Wübben, D.; and Dekorsy, A. 2018 · 2018
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Unsupervised adversarial invariance
Jaiswal, A.; Wu, R. Y.; Abd-Almageed, W.; and Natarajan, P. 2018 · 2018
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Disentangling by Factorising
Kim, H.; and Mnih, A. 2018 · 2018
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Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Liang, S.; Li, Y.; and Srikant, R. 2018 · 2018
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dsprites: Disentanglement testing sprites dataset
Matthey, L.; Higgins, I.; Hassabis, D.; and Lerchner, A. 2017 · 2018
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Cited alongside, same era.
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X.; Duan, Y.; Houthooft, R.; Schulman, J.; Sutskever, I.; and Abbeel, P. 2016 · 2016
Cited alongside, same era.
Disentangling factors of variation in deep representation using adversarial training
Mathieu, M. F.; Zhao, J. J.; Zhao, J.; Ramesh, A.; Sprechmann, P.; and LeCun, Y. 2016 · 2016
Cited alongside, same era.
A survey of inductive biases for factorial representation-learning
Ridgeway, K. 2016 · 2016
Cited alongside, same era.
Deep Variational Information Bottleneck
Alemi, A. A.; Fischer, I.; Dillon, J. V.; and Murphy, K. 2017 · 2017
Cited alongside, same era.
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Hendrycks, D.; and Gimpel, K. 2017 · 2017
Cited alongside, same era.
β \beta -VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Higgins, I.; Matthey, L.; Pal, A.; Burgess, C.; Glorot, X.; Botvinick, M.; Mohamed, S.; and Lerchner, A. 2017 · 2017
Cited alongside, same era.
Invariant representations without adversarial training
Moyer, D.; Gao, S.; Brekelmans, R.; Galstyan, A.; and Ver Steeg, G. 2018 · 2018
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The role of the information bottleneck in representation learning
Vera, M.; Piantanida, P.; and Vega, L. R. 2018 · 2018
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Learning representations for neural network-based classification using the information bottleneck principle
Amjad, R. A.; and Geiger, B. C. 2019 · 2019
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Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation
Cheng, D.; Tu, Y.; Ma, Z.-W.; Niu, Z.; and Zhang, L. 2019 · 2019
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Estimating Information Flow in Deep Neural Networks
Goldfeld, Z. 2019 · 2019
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Caveats for information bottleneck in deterministic scenarios
Kolchinsky, A.; Tracey, B. D.; and Kuyk, S. V. 2019 · 2019
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Nonlinear information bottleneck
Kolchinsky, A.; Tracey, B. D.; and Wolpert, D. H. 2019 · 2019
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The Information Bottleneck: Connections to Other Problems, Learning and Exploration of the IB Curve
Rodriguez Galvez, B. 2019 · 2019
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On the information bottleneck theory of deep learning
Saxe, A. M.; Bansal, Y.; Dapello, J.; Advani, M.; Kolchinsky, A.; Tracey, B. D.; and Cox, D. D. 2019 · 2019
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Learning controllable fair representations
Song, J.; Kalluri, P.; Grover, A.; Zhao, S.; and Ermon, S. 2019 · 2019
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Disentangling latent space for vae by label relevant/irrelevant dimensions
Zheng, Z.; and Sun, L. 2019 · 2019
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Demystifying Inter-Class Disentanglement
Gabbay, A.; and Hoshen, Y. 2020 · 2020
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Invariant Representations through Adversarial Forgetting
Jaiswal, A.; Moyer, D.; Ver Steeg, G.; AbdAlmageed, W.; and Natarajan, P. 2020 · 2020
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The Convex Information Bottleneck Lagrangian
Rodríguez Gálvez, B.; Thobaben, R.; and Skoglund, M. 2020 · 2020
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Understanding the Limitations of Variational Mutual Information Estimators
Song, J.; and Ermon, S. 2020 · 2020
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