Compressed Sensing and Its Applications: Third International MATHEON Conference 2017 (Applied and Numerical Harmonic Analysis)
Boche, Holger; Caire, Giuseppe; Calderbank, Robert; Kutyniok, Gitta; Mathar, Rudolf; Petersen, Philipp
Language: English
Published by Birkhäuser, 2019
Series: Book 79 of 88 - Applied and Numerical Harmonic Analysis
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- Hardcover
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- Title
- Compressed Sensing and Its Applications: Third International MATHEON Conference 2017 (Applied and Numerical Harmonic Analysis)
- Author
- Boche, Holger; Caire, Giuseppe; Calderbank, Robert; Kutyniok, Gitta; Mathar, Rudolf; Petersen, Philipp
- Publisher
- Birkhäuser
- Publication year
- 2019
- Condition
- Very Good
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3319730738
- ISBN 13
- 9783319730738
- Edition
- 1. Auflage
- Series
- Book 79 of 88: Applied and Numerical Harmonic Analysis
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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