Compressed Sensing and Its Applications
Third International MATHEON Conference 2017
Published on 13. August 2019
XVII, 295 pages
978-3-319-73074-5 (ISBN)
Description
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
More details
Series
Edition
1st ed. 2019
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XVII, 295 p. 57 illus., 39 illus. in color.
File size
6,22 MB
ISBN-13
978-3-319-73074-5 (9783319730745)
DOI
10.1007/978-3-319-73074-5
Schweitzer Classification
Other editions
Additional editions
Holger Boche | Giuseppe Caire | Robert Calderbank
Compressed Sensing and Its Applications
Third International MATHEON Conference 2017
Book
08/2019
Birkhäuser
€139.09
Shipment within 10-15 days
Content
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.
