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Compressed Sensing and Its Applications: Third International MATHEON Conference 2017 de Holger Boche

Compressed Sensing and Its Applications: Third International MATHEON Conference 2017: Applied and Numerical Harmonic Analysis

Editat de Holger Boche, Giuseppe Caire, Robert Calderbank, Gitta Kutyniok, Rudolf Mathar, Philipp Petersen
en Limba Engleză Hardback – 14 aug 2019
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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Specificații

ISBN-13: 9783319730738
ISBN-10: 3319730738
Pagini: 295
Ilustrații: XVII, 295 p. 57 illus., 39 illus. in color.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.62 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Birkhäuser
Seria Applied and Numerical Harmonic Analysis

Locul publicării:Cham, Switzerland

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

Highlights state-of-the-art applications and methodologies of compressed sensing Contains chapters written by leading experts in the fields of compressed sensing and deep learning Includes a self-contained introduction to the theory and applications of compressed sensing