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.