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
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. Preface - p. xiii 8. Chapter 1: Energy-based models for inverse imaging problems - p. 1 9. Chapter 2: Good things come in pairs: paired autoencoders for inverse problems - p. 77 10. Chapter 3: Deep learning based reconstruction methods for electrical impedance tomography - p. 127 11. Chapter 4: Recent advances in identification of differential equations from noisy data - p. 177 12. Chapter 5: On the regularizing property of stochastic iterative methods for solving inverse problems - p. 211 13. Chapter 6: Implicit regularization of the deep inverse prior trained with inertia - p. 273 14. Chapter 7: Restarted contractive operators to learn at equilibrium - p. 315
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
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. Preface - p. xiii 8. Chapter 1: Energy-based models for inverse imaging problems - p. 1 9. Chapter 2: Good things come in pairs: paired autoencoders for inverse problems - p. 77 10. Chapter 3: Deep learning based reconstruction methods for electrical impedance tomography - p. 127 11. Chapter 4: Recent advances in identification of differential equations from noisy data - p. 177 12. Chapter 5: On the regularizing property of stochastic iterative methods for solving inverse problems - p. 211 13. Chapter 6: Implicit regularization of the deep inverse prior trained with inertia - p. 273 14. Chapter 7: Restarted contractive operators to learn at equilibrium - p. 315
