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Full Description
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 Perspective
Contents
1. Data-Driven Approaches for Generalized Lasso Problems
2. Implicit Regularization of the Deep Inverse Prior via (Inertial) Gradient Flow
3. Generalized Hardness of Approximation, Hallucinations, and Trustworthiness in Machine Learning for Inverse Problems
4. Energy-Based Models for Inverse Imaging Problems
5. Regularization Theory of Stochastic Iterative Methods for Solving Inverse Problems
6. Advances in Identifying Differential Equations from Noisy Data Observations
7. The Complete Electrode Model for Electrical Impedance Tomography: A Comparative Study of Deep Learning and Analytical Methods
8. Learned Iterative Schemes: Neural Network Architectures for Operator Learning
9. Jacobian-Free Backpropagation for Unfolded Schemes with Convergence Guarantees
10. Operator Learning Meets Inverse Problems: A Probabilistic Perspective
