A Mathematical Introduction to Compressive Sensing
By 慧 廣瀬. 2015
715 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computational Mechanics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Himanshu Tyagi
- Aleksander Madry
- Chinmay Hegde
- Volkan Cevher
- Gábor Lugosi
- Shahar Mendelson
- Clément Louis Canonne
- Jayadev Acharya
In Computational Mechanics
- Gábor Lugosi
- Rachel A. Ward
- Volkan Cevher
- Viraj Shah
- Kfir Yehuda Levy
- Alp Yurtsever
- Chinmay Hegde
- Himanshu Tyagi
Citations in the corpus
- Inference Under Information Constraints I: Lower Bounds From Chi-Square Contraction (2018, methodology): “Since H is symmetric, we can rewrite where the first inequality is by the decoupling inequality E e θ T Mθ ≤ E e θ T Mθ ′ (used in [25] as well) h h ii Since M = H T H is positive semidefinite, we can rewrite and the second uses subgaussianity once again.”
- Risk minimization by median-of-means tournaments (2016, background): “Instead, the alternative we propose is to estimate the difference of the risk for all pairs f, h and organize a two-stage “tournament”.”
- Exact Recovery of Chaotic Systems from Highly Corrupted Data (2016, methodology): “For a comprehensive overview of compressive sensing, we refer the reader to [FR13].”
- Solving Linear Inverse Problems Using Gan Priors: An Algorithm with Provable Guarantees (2018, background): “ squared l2-norm : Definition 2.1. Let S2Rn. Ais m nmatrix. For parameters >0;0, matrix Ais said to satisfy the S-REC(S; ;) if, kA(x 1 x 2)k 2 kx 1 x 2k 2 ; for 8x 1;x 2 2S. Further, based on [37, 38], we propose the following theorem about the convergence of our algorithm: Theorem 2.2. Let G : R”
- Online Adaptive Methods, Universality and Acceleration (2018, methodology): “Due to its appeal as a computationally efficient and fast method, it has found use in numerous applications including: imaging (Chambolle and Pock, 2011), compressed sensing (Foucart and Rauhut, 2013), and deep learning (Sutskever et al.”
- Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors (2018, methodology): “Relation to compressive sensing The view of the gradient estimation problem we developed bears striking similarity to the compressive sensing setting [FR13].”