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Machine Learning Solutions for Inverse Problems: Part a - (Handbook of Numerical Analysis) by Michael Hintermüller (Hardcover)
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Highlights
- Machine Learning Solutions for Inverse Problems: Part A, Volume 26 in the Handbook of Numerical Analysis, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Data-Driven Approaches for Generalized Lasso Problems, Implicit Regularization of the Deep Inverse Prior via (Inertial) Gradient Flow, Generalized Hardness of Approximation, Hallucinations, and Trustworthiness in Machine Learning for Inverse Problems, Energy-Based Models for Inverse Imaging Problems, Regularization Theory of Stochastic Iterative Methods for Solving Inverse Problems, and more.
- Author(s): Michael Hintermüller
- 366 Pages
- Mathematics, Mathematical Analysis
- Series Name: Handbook of Numerical Analysis
Description
Book Synopsis
Machine Learning Solutions for Inverse Problems: Part A, Volume 26 in the Handbook of Numerical Analysis, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Data-Driven Approaches for Generalized Lasso Problems, Implicit Regularization of the Deep Inverse Prior via (Inertial) Gradient Flow, Generalized Hardness of Approximation, Hallucinations, and Trustworthiness in Machine Learning for Inverse Problems, Energy-Based Models for Inverse Imaging Problems, Regularization Theory of Stochastic Iterative Methods for Solving Inverse Problems, and more. Other sections cover Advances in Identifying Differential Equations from Noisy Data Observations, The Complete Electrode Model for Electrical Impedance Tomography: A Comparative Study of Deep Learning and Analytical Methods, Learned Iterative Schemes: Neural Network Architectures for Operator Learning, Jacobian-Free Backpropagation for Unfolded Schemes with Convergence Guarantees, and Operator Learning Meets Inverse Problems: A Probabilistic PerspectiveDimensions (Overall): 8.8 Inches (H) x 6.1 Inches (W) x 1.0 Inches (D)
Weight: 1.55 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 366
Genre: Mathematics
Sub-Genre: Mathematical Analysis
Series Title: Handbook of Numerical Analysis
Publisher: Academic Press
Format: Hardcover
Author: Michael Hintermüller
Language: English
Street Date: October 28, 2025
TCIN: 1006749677
UPC: 9780443417894
Item Number (DPCI): 247-27-9511
Origin: Made in the USA or Imported
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