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The information bottleneck principle provides an information-theoretic method for representation learning, by training an encoder to retain all information which is relevant for predicting the label while minimizing the amount of other, excess information in the representation.
Self-organization in a perceptual network
R. Linsker · 1988
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The information bottleneck method
Naftali Tishby, Fernando C. Pereira, and William Bialek · 2000
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The im algorithm: A variational approach to information maximization
David Barber and Felix Agakov · 2003
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The mir flickr retrieval evaluation
Mark J. Huiskes and Michael S. Lew · 2008
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
XuanLong Nguyen, Martin J. Wainwright, and Michael I. Jordan · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei · 2009
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Multimodal deep learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y. Ng · 2011
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How do humans sketch objects?
Mathias Eitz, James Hays, and Marc Alexa · 2012
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Deep neural networks for acoustic modeling in speech recognition
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Brian Kingsbury, et al · 2012
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Imagenet classification with deep convolutional neural networks
Ilya Sutskever, Geoffrey E Hinton, and A Krizhevsky · 2012
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Deep canonical correlation analysis
Galen Andrew, Raman Arora, Jeff Bilmes, and Karen Livescu · 2013
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Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval
Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin · 2013
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Multilingual Distributed Representations without Word Alignment
Karl Moritz Hermann and Phil Blunsom · 2014
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Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 2014
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2014
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Improved multimodal deep learning with variation of information
Kihyuk Sohn, Wenling Shang, and Honglak Lee · 2014
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Multimodal learning with deep boltzmann machines
Nitish Srivastava and Ruslan Salakhutdinov · 2014
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Variational Inference with Normalizing Flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Cited alongside, same era.
A relationship between the average precision and the area under the roc curve
Wanhua Su, Yan Yuan, and Mu Zhu · 2015
Cited alongside, same era.
Deep Learning and the Information Bottleneck Principle
Naftali Tishby and Noga Zaslavsky · 2015
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Cited alongside, same era.
Sketch-based image retrieval via siamese convolutional neural network
Y. Qi, Y. Song, H. Zhang, and J. Liu · 2016
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The sketchy database: learning to retrieve badly drawn bunnies
Patsorn Sangkloy, Nathan Burnell, Cusuh Ham, and James Hays · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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MINE: Mutual Information Neural Estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and R Devon Hjelm · 2018
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Formal Limitations on the Measurement of Mutual Information
David McAllester and Karl Stratos · 2018
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VAE with a VampPrior
Jakub M. Tomczak and Max Welling · 2018
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Representation Learning with Contrastive Predictive Coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Generative domain-migration hashing for sketch-to-image retrieval
Jingyi Zhang, Fumin Shen, Li Liu, Fan Zhu, Mengyang Yu, Ling Shao, Heng Tao Shen, and Luc Van Gool · 2018
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Deep Variational Canonical Correlation Analysis
Weiran Wang, Xinchen Yan, Honglak Lee, and Karen Livescu · 2016
Cited alongside, same era.
Deep Variational Information Bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy · 2017
Cited alongside, same era.
Variational Lossy Autoencoder
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2017
Cited alongside, same era.
PixelVAE: A Latent Variable Model for Natural Images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval
Li Liu, Fumin Shen, Yuming Shen, Xianglong Liu, and Ling Shao · 2017
Cited alongside, same era.
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Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Probabilistic symmetry and invariant neural networks
Benjamin Bloem-Reddy and Yee Whye Teh · 2019
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Semantically tied paired cycle consistency for zero-shot sketch-based image retrieval
Anjan Dutta and Zeynep Akata · 2019
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Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier J. Hénaff, Ali Razavi, Carl Doersch, S. M. Ali Eslami, and Aaron van den Oord · 2019
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Invariant Information Clustering for Unsupervised Image Classification and Segmentation
Xu Ji, João F. Henriques, and Andrea Vedaldi · 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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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Contrastive Multiview Coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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On Mutual Information Maximization for Representation Learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, and Mario Lucic · 2019
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Deep multi-view information bottleneck
Qi Wang, Claire Boudreau, Qixing Luo, Pang-Ning Tan, and Jiayu Zhou · 2019
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