Generalization of a prior deep unrolled SBL architecture beyond Gaussian matrices

Determine the performance of the deep unrolling–based SBL architecture proposed by Chandra et al. when applied to measurement matrices beyond i.i.d. Gaussian ensembles, including structured or correlated dictionaries.

Background

In discussing prior deep learning approaches that unroll SBL, the paper notes that one such architecture scales in complexity with the number of snapshots and measurement dimension, requiring retraining when these change. For fixed dimensions, that prior work showed generalization across i.i.d. Gaussian sensing matrices.

The authors explicitly state that the performance of that prior model on other families of sensing matrices remains unknown, highlighting a gap in understanding its generalization beyond Gaussian ensembles.

References

For a fixed L and M, the authors showcase the generalization capabilities of the model to matrices whose elements are sampled from the standard Gaussian distribution but performance of the model beyond this class of matrices is unknown.

— Sparse Bayesian Learning Algorithms Revisited: From Learning Majorizers to Structured Algorithmic Learning using Neural Networks  (2604.02513 - Balaji et al., 2 Apr 2026) in Introduction, Deep Learning for SSR (paragraph discussing prior work [chandra])