arXiv:2602.03855v2 Announce Type: replace-cross Abstract: Inverse problems are often ill-posed and require optimization schemes with strong stability and convergence guarantees. While learning-based approaches such as deep unrolling and meta-learning achieve strong empirical performance, they typically lack explicit control over descent and curvature, limiting robustness. We propose a learned Majorization-Minimization (MM) framework for inverse problems within a bilevel optimization setting. Instead of learning a full optimizer, we learn a structured curvature majorant that governs each MM ste
The proliferation of deep learning in inverse problems necessitates more robust and controllable optimization methods, addressing limitations seen in current learning-based approaches.
Improving the stability and convergence guarantees of learning-based solutions for inverse problems, such as medical imaging (EEG) and other scientific applications, enhances reliability and expands AI's applicability in critical domains.
This research introduces a more structured, theoretically sound framework for incorporating deep learning into inverse problem solving, potentially leading to safer and more predictable AI systems in fields requiring high accuracy.
- · AI researchers in inverse problems
- · Medical imaging companies
- · Healthcare providers
- · Patients requiring advanced diagnostics
- · Developers of less robust, purely empirical deep learning methods
More accurate and reliable AI-driven solutions for complex scientific and medical inverse problems become feasible.
This improved reliability could accelerate regulatory approval and clinical adoption of AI-enhanced diagnostic tools.
Increased trust in AI's foundational algorithms could foster broader integration into other sensitive areas like defense and critical infrastructure monitoring.
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Read at arXiv cs.LG