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Compressed sensing and its applicati...

International MATHEON Conference on Compressed Sensing and Its Applications (2017 :)

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Compressed sensing and its applications = third International MATHEON Conference 2017 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Compressed sensing and its applications/ edited by Holger Boche ... [et al.].
Reminder of title:	third International MATHEON Conference 2017 /
other author:	Boche, Holger.
corporate name:	International MATHEON Conference on Compressed Sensing and Its Applications
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xvii, 295 p. :ill., digital ;24 cm.
[NT 15003449]:	An Introduction to Compressed Sensing -- Quantized Compressed Sensing: a Survey -- On reconstructing functions from binary measurements -- Classification scheme for binary data with extensions -- Generalization Error in Deep Learning -- Deep learning for trivial inverse problems -- Oracle inequalities for local and global empirical risk minimizers -- Median-Truncated Gradient Descent: A Robust and Scalable Nonconvex Approach for Signal Estimation -- Reconstruction Methods in THz Single-pixel Imaging.
Contained By:	Springer Nature eBook
Subject:	Compressed sensing (Telecommunication) - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-319-73074-5
ISBN:	9783319730745

Compressed sensing and its applications = third International MATHEON Conference 2017 /
Compressed sensing and its applicationsthird International MATHEON Conference 2017 /[electronic resource] :edited by Holger Boche ... [et al.]. - Cham :Springer International Publishing :2019. - xvii, 295 p. :ill., digital ;24 cm. - Applied and numerical harmonic analysis,2296-5009. - Applied and numerical harmonic analysis..

An Introduction to Compressed Sensing -- Quantized Compressed Sensing: a Survey -- On reconstructing functions from binary measurements -- Classification scheme for binary data with extensions -- Generalization Error in Deep Learning -- Deep learning for trivial inverse problems -- Oracle inequalities for local and global empirical risk minimizers -- Median-Truncated Gradient Descent: A Robust and Scalable Nonconvex Approach for Signal Estimation -- Reconstruction Methods in THz Single-pixel Imaging.

The chapters in this volume highlight the state-of-the-art of compressed sensing and are based on talks given at the third international MATHEON conference on the same topic, held from December 4-8, 2017 at the Technical University in Berlin. In addition to methods in compressed sensing, chapters provide insights into cutting edge applications of deep learning in data science, highlighting the overlapping ideas and methods that connect the fields of compressed sensing and deep learning. Specific topics covered include: Quantized compressed sensing Classification Machine learning Oracle inequalities Non-convex optimization Image reconstruction Statistical learning theory This volume will be a valuable resource for graduate students and researchers in the areas of mathematics, computer science, and engineering, as well as other applied scientists exploring potential applications of compressed sensing.

ISBN: 9783319730745

Standard No.: 10.1007/978-3-319-73074-5doiSubjects--Topical Terms:

3274852
Compressed sensing (Telecommunication)
--Congresses.

LC Class. No.: TK5102.9 / .I57 2017

Dewey Class. No.: 621.3822

Compressed sensing and its applications = third International MATHEON Conference 2017 /
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505 0 $a An Introduction to Compressed Sensing -- Quantized Compressed Sensing: a Survey -- On reconstructing functions from binary measurements -- Classification scheme for binary data with extensions -- Generalization Error in Deep Learning -- Deep learning for trivial inverse problems -- Oracle inequalities for local and global empirical risk minimizers -- Median-Truncated Gradient Descent: A Robust and Scalable Nonconvex Approach for Signal Estimation -- Reconstruction Methods in THz Single-pixel Imaging.
520 $a The chapters in this volume highlight the state-of-the-art of compressed sensing and are based on talks given at the third international MATHEON conference on the same topic, held from December 4-8, 2017 at the Technical University in Berlin. In addition to methods in compressed sensing, chapters provide insights into cutting edge applications of deep learning in data science, highlighting the overlapping ideas and methods that connect the fields of compressed sensing and deep learning. Specific topics covered include: Quantized compressed sensing Classification Machine learning Oracle inequalities Non-convex optimization Image reconstruction Statistical learning theory This volume will be a valuable resource for graduate students and researchers in the areas of mathematics, computer science, and engineering, as well as other applied scientists exploring potential applications of compressed sensing.
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