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Compressed sensing and its applicati...

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Compressed sensing and its applicationsthird International MATHEON Conference 2017 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Compressed sensing and its applicationsedited by Holger Boche ... [et al.].
Reminder of title:	third International MATHEON Conference 2017 /
other author:	Boche, Holger.
corporate name:
Published:	Cham :Springer International Publishing :2019.
Description:	xvii, 295 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Compressed sensing (Telecommunication)
Online resource:	https://doi.org/10.1007/978-3-319-73074-5
ISBN:	9783319730745$q(electronic bk.)

Compressed sensing and its applicationsthird 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$q(electronic bk.)

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

769143
Compressed sensing (Telecommunication)

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

Dewey Class. No.: 621.3822

Compressed sensing and its applicationsthird 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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