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Compressed sensing techniques enable efficient acquisition and recovery of sparse, high-dimensional data signals via low-dimensional projections.
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Statistical compressed sensing of Gaussian mixture models
Guoshen Yu and Guillermo Sapiro · 2011
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Communications-inspired projection design with application to compressive sensing
William R Carson, Minhua Chen, Miguel RD Rodrigues, Robert Calderbank, and Lawrence Carin · 2012
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Representation learning: A review and new perspectives
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Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent · 2013
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Amortized inference in probabilistic reasoning
Samuel Gershman and Noah Goodman · 2014
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Reconnet: Non-iterative reconstruction of images from compressively sensed measurements
Kuldeep Kulkarni, Suhas Lohit, Pavan Turaga, Ronan Kerviche, and Amit Ashok · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Variational information maximization for feature selection
Shuyang Gao, Greg Ver Steeg, and Aram Galstyan · 2016
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
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Deep variational information bottleneck
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Auto-encoding variational Bayes
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What regularized auto-encoders learn from the data-generating distribution
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Nonlinear information-theoretic compressive measurement design
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Gradient estimation using stochastic computation graphs
John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel · 2015
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Human-level concept learning through probabilistic program induction
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Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2017
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Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
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One network to solve them all—solving linear inverse problems using deep projection models
JH Rick Chang, Chun-Liang Li, Barnabas Poczos, BVK Vijaya Kumar, and Aswin C Sankaranarayanan · 2017
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Infogail: Interpretable imitation learning from visual demonstrations
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Amortized inference regularization
Rui Shu, Hung H Bui, Shengjia Zhao, Mykel J Kochenderfer, and Stefano Ermon · 2018
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Modeling sparse deviations for compressed sensing using generative models
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Fixing a broken ELBO
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The information autoencoding family: A lagrangian perspective on latent variable generative models
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Convcsnet: A convolutional compressive sensing framework based on deep learning
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Compressed sensing with deep image prior and learned regularization
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Global guarantees for enforcing deep generative priors by empirical risk
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On the convergence of learning-based iterative methods for nonconvex inverse problems
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Infovae: Information maximizing variational autoencoders
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