Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
Published in arXiv Preprint, 2025
Analyzes how deep linear neural networks trained via gradient descent with weight decay automatially adapt to structure in data from inverse problems.
Joint work with Hannah Laus, Vasilis Charisopoulos, Felix Krahmer, and Rebecca Willett.