Compressed Sensing and Its Applications
Third International MATHEON Conference 2017
Nonfiction, Science & Nature, Mathematics, Mathematical Analysis, Applied
| Author: | ISBN: | 9783319730745 | |
| Publisher: | Springer International Publishing | Publication: | August 13, 2019 |
| Imprint: | Birkhäuser | Language: | English |
| Author: | |
| ISBN: | 9783319730745 |
| Publisher: | Springer International Publishing |
| Publication: | August 13, 2019 |
| Imprint: | Birkhäuser |
| Language: | English |
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.
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.
