Deep-Layer Limit and Convergence of FBS-Induced Networks in Learning Problems
A new paper on arXiv (2605.27133) extends research on deep unfolding neural networks derived from the forward-backward-splitting (FBS) algorithm. The study analyzes the most basic FBS-induced network, unrolled from the original algorithm with direct parameter relaxations. Under mild assumptions, the authors establish a general convergence property for the training problem, showing that it approaches the learning problem of the deep-layer limit system via a Γ-convergence argument. This work continues prior forward system analyses using difference/differential inclusion formulations.
Key facts
- Paper arXiv:2605.27133, announcement type: cross
- Focuses on deep unfolding neural networks from iterative optimization schemes and ODEs/PDEs
- Network architecture derived from basic forward-backward-splitting (FBS) algorithm
- Incorporates direct parameter relaxations into the unrolled FBS algorithm
- Establishes convergence property of training problem to deep-layer limit system
- Uses Γ-convergence argument for cluster points
- Builds on previous forward system analyses with difference/differential inclusions
- Published on arXiv with abstract available
Entities
Institutions
- arXiv