Compressed Sensing

By Gitta Kutyniok. 2012

17,863 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Networks and Communications, Numerical Methods & Scientific Computing, Information Theory

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In Computational Mechanics

In Computer Networks and Communications

In Numerical Methods & Scientific Computing

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Citations in the corpus

GAN Inversion: A Survey (2021, background): “, the signal x∗ ∈ S is sufficiently sparse and measurement matrix A satisfies certain algebraic conditions, such as the restricted isometry property (RSP) [136] or the restricted eigenvalue condition (REC) [137].”
Unsupervised Machine Translation Using Monolingual Corpora Only (2017, background): “Note however that the decoder could still produce a bad translation while yielding a valid sentence in the target domain, as constraining the encoder to map two languages in the same feature space does (1)Even without attention, reconstruction can be surprisingly easy, depending on the length of the”
An overview of deep learning in medical imaging focusing on MRI (2018, background): “MRF is closely related to the idea of compressed sensing [151] in MRI [152] in that MRF undersamples data in k-space producing aliasing artifacts in the reconstructed images that can be suppressed by compressed 27”
Discovering governing equations from data by sparse identification of nonlinear dynamical systems (2015, neutral): “Proc Natl Acad Sci USA 110(17):6634–6639.”
Memory Bounded Deep Convolutional Networks (2014, background): “It has been shown for some classes of machine learning models that regularization terms consisting of an `1 norm can provide a provably tight approximation, or even an exact solution, to a corresponding `0 regularization that directly penalizes or constrains the number of nonzero parameters of the m”
Topology and Geometry of Half-Rectified Network Optimization (2016, methodology): “It is a form of n-width similar to Kolmogorov width Donoho (2006) and is also related to robust sparse coding from Tang et al. (2013); Ekanadham et al.”
A Singular Value Thresholding Algorithm for Matrix Completion (2008, background): “To simplify, we will assume that this convex set is given by where the f i ’s are convex functionals (note that one can handle linear equality constraints by considering pairs of aﬃne functionals).”
FPNN: Field Probing Neural Networks for 3D Data (2016, methodology): “Our method can be viewed as an efficient scheme of sparse coding[7].”
LOGAN: Latent Optimisation for Generative Adversarial Networks (2019, methodology): “Inspired by compressed sensing (Candes et al., 2006; Donoho, 2006), Wu et al. (2019) introduced latent optimisation for GANs.”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, methodology): “They show that this learning task can be casted as a Compressed Sensing [66] problem and they use the ECOC scheme to generate a sufﬁcient number of binary classiﬁers given the set of classes.”
Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser (2020, background): “Compressive sensing [49, 50] provides a set of theoretical results regarding recovery of sparse signals from a small number of linear measurements.”
Uncertainty Principles and Vector Quantization (2006, neutral): “Such representations will be called Kashin’s representations.”
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser (2021, background): “Compressive sensing [56, 57] aims to recover signals from a small number of (typically, random) linear measurements.”
KALE: When Energy-Based Learning Meets Adversarial Training (2020, background): “(Wu et al., 2019b) is motivated by the compressed sensing framework (Donoho, 2006; Cand`es et al., 2006), where the latent noise is optimized during training to minimize a measurement error.”
Minimax Estimation of Functionals of Discrete Distributions (2014, background): “Following this rationale, Donoho and Johnstone [33] proposed the soft-thresholding estimator to estimate the normal mean given that we know a priori that the mean θ lies in a `p ball, p ∈ (0,∞).”